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Keye Technology leads innovation, based on two modules of deep learning, supervised and unsupervised, combined with practical experience, to independently develop the core technology of Industrial Vision inspection AI 2.0 era - "Good Product Training Model". It is divided into anomaly inspection and anomaly defect inspection, which can efficiently deal with problems such as small defect samples, long model construction cycle, low product changeover efficiency, and complicated labeling process.   The core advantages of good product training are that major defects do not require labeling, product model can be quickly changed, sample training time is fast, unknown defects can be detected, defect sample collection time is short, and serious defects are not missed. The anomaly detection model only needs to use good quality images to perform pixel level detection and whole image classification of known and unknown defects, achieving fast online verification.   The anomaly defect detection model is designed to address small defect scenarios by adding annotated data to further optimize the detection model and effectively enhance detection flexibility.   The combination of the good product inspection model with existing defect models, classification models, segmentation models, and object detection models significantly improves detection performance and comprehensive recognition capabilities. No matter the size of the defect, known or unknown, it can achieve fast and accurate identification, reduce the missed detection rate, and provide strong guarantees for the stable operation of the production line and product quality. The upgraded bidirectional training model can better facilitate industrial vision inspection and provide higher detection efficiency.

High quality defect data is crucial for training and optimizing AI visual inspection models on industrial production lines. To obtain rare and high-quality defect samples for product appearance defect detection, multiple layers of effort are required. Artificially creating product defect samples - low authenticity! High yield rate and low defect rate, online collection of defect samples - time-consuming! Product changeover, collecting a large number of defect samples in a short period of time - difficult! Sample defects are complex and diverse, and collection is cumbersome - low efficiency!   Defect Generation By using AI visual inspection and diffusion modeling techniques, various types, positions, and shapes of defect images can be simulated with only a small number of sample images through forward and backward diffusion algorithms. Simulated defect images are highly similar in appearance and features to actual defects, providing an effective solution to the problem of defect data scarcity.   Significant role of key nodes Rapid model construction-Rare defect samples are few, and defects can be generated through "defect synthesis" to achieve rapid model construction in AI visual inspection. Rapid deployment of models-Product changeover can utilize "defect synthesis" to quickly generate defects in related products for model training and rapid deployment. Rapid improvement of the model-When there are missed defects on the production line, simulation defects can be generated through "defect synthesis" to quickly reduce production missed defects.   Intelligent, efficient, and easy to operate Defect synthesis "only requires three steps of marking defective samples, placing good products, and defect synthesis to generate a large number of high-quality defect maps, expand the training sample set, significantly shorten sample collection time, and achieve fast model training. Strong performance and outstanding advantages Strong applicability- Can be used for products and different defects in multiple industry sectors Strong flexibility- Independently choose defect location, quantity, and type to meet personalized user needs Easy to operate- Three step generation of defect maps, significantly saving time and cost High Collaboration- The generated results come with annotation information, without the need for secondary annotation, and can be directly used for model training Excellent effect- The generated defects are highly similar to real defects, greatly improving the training effectiveness of the model in AI visual inspection field.   Efficient generation of simulation defects Bottle cap: logo print Bottle cap: stains Bottle cap: black spot Generative AI plays an important role in the field of industrial vision. Through intelligent defect generation technology, KeyeTech Skill quickly generates a large number of defect images that are close to reality, solving the problem of scarce defect samples and time-consuming and laborious collection, greatly improving the training efficiency and generalization ability of AI visual inspection model! Click here for more AI VISUAL INSPECTION VIDEOS

Technological innovation is a core element in developing new quality productive forces. Continuously strengthening innovation in artificial intelligence technology is essential for achieving high-level technological self-reliance and self-improvement. Labor materials with higher technological content provide a powerful source of motivation for producing new quality productive forces. KeyeTech is deeply engaged in the artificial intelligence sector, continuously exploring and researching algorithms and computing power. In 2023, KeyeTech successfully developed its own AI computing unit, significantly accelerating the progress of visual inspection. In 2024, KeyeTech also achieved significant breakthroughs in algorithm research... "It" the Efficient Assistant for Annotation In the era of big data, data is undoubtedly a valuable resource. However, efficiently and accurately annotating vast amounts of data has become a significant challenge. Traditional manual annotation methods are inefficient and prone to errors. The application of KeyeTech's automatic annotation function is like a timely rain, opening new doors for data annotation. Automatic annotation is based on deep learning and natural language processing technologies, capable of automatically recognizing text and image data. With just a click of the mouse, it can accurately identify and annotate sample defects, greatly improving annotation efficiency and significantly enhancing annotation quality. "It" the Data Screening Steward Compared to traditional training methods, KeyeTech places greater emphasis on meticulous data screening and strict quality control. Only positive samples that meet the requirements are selected for training, avoiding interference from incorrect or unexpected data during model training, thereby improving the accuracy and reliability of the model. The core of positive sample training lies in its ability to learn from good product samples, enabling the machine to accurately identify and output correct training results, significantly enhancing recognition capabilities and data output accuracy. A New Pathway Toward the Intelligent Era With continuous technological innovation and the expansion of application scenarios, artificial intelligence is demonstrating limitless potential. For intelligent enterprises, optimizing upgrades, reforming innovations, and self-research production are crucial for development and progress. Looking to the future, artificial intelligence will widely empower more fields, injecting vitality into the intelligent era. KeyeTech will refine its "skill value," unlock its "future power," and continue to delve into artificial intelligence, embarking on a new pathway toward intelligence and opening a new chapter of wisdom!  

This is the best era for AI Voice recognition, facial recognition unlocking... AI has penetrated every corner of our lives. In recent years, the application of AI is no longer limited to daily life; it has gradually begun to be implemented in the production systems of various industries, such as visual inspection, intelligent sorting, automatic packaging, and more. However, for industry applications, integrating AI into enterprise production models to improve production efficiency is not a smooth process and faces several challenges and difficulties.   In the industrial sector regarding visual inspection: Companies generate massive amounts of data during production processes, but how can they filter out valuable data? How can AI deep learning further enhance control and inspection accuracy? How to acquire and represent industry knowledge, and how to convert it into data that participates in AI calculations and creates value? These challenges in applying AI to industries require continuous innovation and upgrades to AI platforms to address them. 1.Industry Applications: Huge AI Challenges In the industrial field, especially in visual inspection, many AI applications face the contradiction of having limited sample data while the accuracy requirements remain high. Various sensors distributed across production equipment generate vast amounts of inspection data daily. However, the final quality of the product is jointly influenced by thousands of parameters such as process parameters, material properties, and production equipment—only a small proportion of the inspection data samples is directly meaningful for quality prediction analysis. Furthermore, most companies are still relying on manual inspection, with widespread concerns that AI devices may not be as flexible as human inspection. In the face of these challenges for industry applications, there is a need for a comprehensive and efficient AI machine that meets the demands of various industries for limited samples and high flexibility. 2.KeyeTech: Leading Capability in Industry AI Implementation KeyeTech has developed AI image visual inspection equipment through years of continuous innovation and upgrades, enabling machines to possess collaborative and cognitive abilities similar to those of humans. Its AI platform can simulate massive data with a small number of samples, labeling and analyzing samples to build inspection models. It is also equipped with advantages such as high efficiency, stability, and the ability to switch between multiple models, solving issues related to the accuracy, efficiency, safety, and costs associated with manual inspection. 01 Quality Guardian In light of the limited sample data faced by enterprises, KeyeTech’s AI platform, based on deep learning, has data augmentation capabilities that allow a small number of samples to simulate massive data, thereby constructing its own AI database. When the equipment is in operation and detects a defective product, it quickly eliminates defect, ensuring that the products leaving the factory are defect-free.   02  Professional Image Inspector Customized LED light sources and industrial cameras are equipped with an intelligent processing platform. When the products to be tested enter the inspection area, the industrial camera can capture multiple photos in about 0.1 seconds. Through AI algorithms, it compares against the sample database, simulating human thought processes to conduct quality inspections on product appearances. Issues such as black edge, material deficiency, and deformation of bottles are quickly recognized and eliminated.   03 Safety Guardian Currently, KeyeTech's  AI visual inspection machine is applied in industries such as packaging, food, 3C (computer, communication, consumer electronics), and medical fields. By leveraging AI for quality inspection and classification, it can reduce the number of inspection personnel by two-thirds.  

In recent years, the concept of AI has been widely accepted by the public, and AI visual inspection is gradually being applied in the industrial sector for the detection of product appearance defects. Appearance defects in products can negatively impact aesthetics, comfort, and overall performance. This is why manufacturing companies are keen to detect these flaws promptly, allowing them to control quality and enhance the product's added value. While the importance of algorithms in AI visual inspection is often emphasized, the lighting source directly affects image quality. The primary role of the lighting in AI visual inspection equipment is to overcome the interference from ambient light, ensuring image stability and achieving the highest possible contrast. High-contrast imaging makes it easier to detect product defects. At KeyeTech, our AI image recognition equipment not only employs deep learning-based algorithms but also features our self-developed optical imaging system.   Design of Imaging Angles and Intensity in 3D Space Currently, our AI image recognition devices utilize an optical imaging system where the lighting source configurations are categorized based on illumination methods: bright field and dark field illumination, structured light and strobe light. These methods work in conjunction with both the surface and internal characteristics of the objects being inspected, enabling comprehensive relational analysis. By projecting the light source onto the inspected objects and analyzing the distortions produced, we can demodulate the 3D information of the objects, allowing for precise location of product defects.   Multispectral Imaging from Ultraviolet to Infrared KeyeTech's optical imaging system employs a full spectrum of ultraviolet to infrared, including polarized and fluorescent methods, to achieve comprehensive 360° detection of the objects. Defects invisible to the naked eye can be revealed with complete clarity, ensuring no blind spots or overlooked areas.   Energy-Efficient and Durable Our optical imaging system uses LED lighting, which is compact, energy-efficient, and features fast response times. It also offers excellent monochromaticity, high reliability, and uniform, stable light output, making it easy to integrate for optimal imaging results on products.

According to statistics, the growth rate of production in the beverage industry has generally remained between 10% and 20% since 2001. The robust demand in the beverage market has brought significant benefits to the bottle cap industry. According to a report by Market Research Future, it is estimated that the global cap and closure market will reach approximately $91.7189 billion by 2030, from 2022 to 2030. For many bottle cap companies, facing such a vast market, the production lines are gradually evolving towards digitalization and informatization. Among these developments, intelligent production and processing equipment provide crucial technical support for achieving "non-destructive" and "precise" bottle caps. Quality Inspection in the Bottle Cap Industry Needs AI EmpowermentWith the gradual development of intelligent production lines in bottle cap companies, the demand for machine vision technology has become increasingly common. During the production process of bottle caps, defects such as black spots, gaps, flash, and deformation are inevitable. Relying on manual inspection or traditional visual inspection makes it challenging to meet the ever-growing quality demands of businesses. Small scratches or tiny gaps on the caps are often hard to detect. During the manual inspection process, it is time-consuming and costly, yet it often fails to meet detection standards. This directly results in inconsistent product quality upon delivery, leading to low efficiency. Therefore, the AI-powered quality inspection has become an inevitable trend for businesses to save costs and improve productivity. AI Empowerment Makes Bottle Cap Inspection More EfficientAlthough bottle caps are small, they play a crucial role in food and beverage packaging. They not only make products easier to carry and prevent direct exposure to external air but also act as "taste guardians" in the realm of carbonated drinks—ensuring that carbon dioxide does not escape. Consequently, detecting defects on caps is especially important, as the quality of inspection directly impacts the quality of subsequent beverage filling processes. Bottle caps produced by injection molding machines inevitably exhibit defects such as black spots, color differences, impurities, threads, sealing rings, broken rings, gaps, flash, burrs, deformation, dimensions, gaskets, inner seals, mold numbers, etc. The severity of these flaws can vary, and they may appear on different parts of the cap, such as the sides, top, or concave areas. Therefore, relying on manual vision inspection alone can easily lead to missed or incorrect detection. KeyeTech employs AI image vision inspection tailored to product characteristics and customer detection requirements, creating customized inspection plans. Before testing, samples of bottle caps are collected, labeled, and utilized to optimize training models. Based on actual customer needs, algorithm deployment models are established. During the inspection process, uniform lighting and industrial cameras capture and identify the external appearance, while the software system classifies and makes decisions. When a bottle cap enters the inspection area through a cap feeding device, the light source and camera are triggered to capture and identify it. The software system will classify and decide on the cap based on the trained model. If a non-conforming product is detected, it will show "NG" (not good) and issue a removal command; if it is a conforming product, it will display "OK" and count it for sorting into boxes. Quality determines the development of a company, and good product quality enhances corporate efficiency. KeyeTech is committed to implementing AI to ensure quality for enterprises, becoming a guardian of quality in specialized industries.    

The modern computer vision technology based on artificial intelligence and deep learning methods has made significant progress in the past decade. Today, it is widely used for image classification, facial recognition, and the recognition of objects within images. So, what exactly is deep learning? How is deep learning applied in visual inspection? What is Deep Learning? Deep learning is a branch of machine learning techniques that consists of classifiers built from artificial neural networks. The principle behind it is to teach machines to learn through examples to provide labeled examples of specific types of data to the neural network. The model extracts common patterns from these examples and converts them into a neural network model containing this information, which aids in classifying information obtained in the future. Visual inspection based on deep learning technology can achieve localization, differentiation of defects, character recognition, and more, simulating human visual inspection during operation. What does this actually mean? For example, if we want to create visual inspection software for inspecting lithium batteries, we need to develop a deep learning-based algorithm and train it using examples of the defects that need to be detected. With the data of defects, the neural network will ultimately detect them without any additional instructions. Visual inspection systems based on deep learning are adept at detecting defects with complex characteristics. They can address not only complex surface and appearance defects but also generalize and conceptualize the surface of lithium batteries. What is a Convolutional Neural Network? When it comes to visual inspection based on deep learning, the most commonly mentioned technology is the Convolutional Neural Network (CNN). So, what exactly is a CNN? A Convolutional Neural Network, or CNN, possesses special features that retain spatial information in the network, making it better suited for image classification problems. Its principles are inspired by biological data from human vision, where vision is based on multiple cortical layers, and each layer recognizes increasingly complex structured information. What we perceive consists of many individual pixels; then, geometric compositions are recognized from these pixels, followed by more complex elements, such as objects, faces, human bodies, and animals. Keye Technology's AI image visual inspection utilizes convolutional neural network, focusing more on network cascades, designing different cascaded network methods tailored to different scenarios, which accurately reflects image features to enhance precision during visual inspection. How to Integrate an AI Visual inspection System? 01 Requirements Clarification Integrating an AI visual inspection system typically starts with a business and technical analysis. First, it is essential to clarify what types of defects the system should detect and under what environmental conditions it will be used. 02 Data Collection and Preparation Before developing a deep learning model, data needs to be collected and prepared. Keye Technology has built a robust and rich algorithm library through more than a decade of continuous development and optimization. When faced with the inspection of new products, the algorithm library can be leveraged for incremental/transfer learning, where a small number of new samples are added to the original training results, significantly shortening the training time for new products and enabling rapid learning. 03 Training and Evaluation After collecting the new samples, the next step is to train, validate, and evaluate the performance and accuracy of the model's results. 04 Deployment and Improvement When deploying a visual inspection model, it is crucial to consider how the software and hardware system architecture correspond to the model's capacity. Application Cases of AI Visual inspection Systems Packaging Containers: Suitable for quality control of products, used to detect external defects such as black spots, burrs, gaps, and mold numbers. Lithium Batteries: In the production of lithium batteries, common defects such as pinholes, sand holes, scratches, unevenness, and improper welding often occur during processes like seal stud welding and top cover welding.  

With the rapid implementation of artificial intelligence technology and the continuous development of the intelligent robot industry, visual inspection machines are unleashing even stronger vitality. The typical structure of visual inspection equipment design mainly consists of five parts, namely: lighting, lens, camera, image acquisition, and computing hardware units.   What is the visual inspection? Visual inspection system refers to the use of machine vision products (i.e. image capture devices, divided into CMOS and CCD) to convert the captured target into an image signal, which is transmitted to a dedicated image processing system and converted into a digital signal based on pixel distribution, brightness, color, and other information; The image system performs various operations on these signals to extract features of the target, and then controls the on-site equipment actions based on the discrimination results.     Customization process of visual system 1. Software Testing The cyclic process of ensuring the correctness of software processes and the correct application logic relationships, discovering vulnerabilities in the system, conducting research and development modifications, and testing verification. 2. Hardware testing Conduct hardware reliability testing on the hardware itself (aging testing, compatibility testing, failure rate testing) and the environment to determine whether the software can run in multiple hardware configuration environments. 3. Joint debugging test Test the software and hardware joint debugging function to verify the correctness of electrical and software signal communication logic, light source, camera and other hardware triggering functions such as photography and scanning, as well as the statistics of detection results. 4. Model testing   Focus on the functional testing, performance testing, evaluation of model indicators, and analysis of indicator results of the model.     How to carry out testing of visual inspection system? Customer Requirements Application type: Accurately and detailedly understand the changes in product testing standards, external dimensions, and other factors that affect testing, and preliminarily evaluate whether they can meet the requirements. Stage requirements: Customers' demands for visual inspection efficiency, quantifying the time required for visual inspection steps. Accuracy requirement: Control the accuracy of product defect detection. Installation space: Confirm if there are any restrictions on the installation of visual equipment in the on-site environment.   Conceptual design Requirement analysis: Organize key customer requirements and analyze their feasibility. Hardware design: Selection of visual system platform, camera, lens, and light source. Software design: Use third-party visual software or develop visual processing software independently. Feasibility verification: Set up software and hardware environments, customize human-computer interaction interfaces, and conduct preliminary testing to determine if they can meet customer needs.   Algorithm Deployment Cloud platform development: Collect product defect sample images, upload and store images, select images, annotate, upload, train, test, optimize, and apply.  

When encountering defects in bottles, lids, straws, etc What testing methods do you know? Human eye recognition? leak hunting? But do you know that this method has a very high false detection rate? Is there a safe and efficient detection method? A group of innovative pioneers have found the answer AI visual defect detection From manual checking to traditional vision inspection, and then to AI, the revolution of defect detection technology Before the AI visual defect inspection scheme was developed, the defect detection of packaging containers, such as black spots, dirt, flash edges, and missing materials on beverage bottles, mainly relied on manual inspection. Faced with high-intensity repetitive work, manual inspection inevitably leads to errors, and when new defect problems arise, it is difficult for people to effectively and quickly identify them. However, the market environment is fiercely competitive, and even 0.1% of product defects are not allowed. In order to ensure high quality of products in production, more efficient defect detection solutions must be used.   Faced with the urgent demand of the market, Keye Technology has focused on segmented fields in the past decade and successfully developed diversified AI visual defect detection solutions, leading the world in defect detection technology.   KeyeTech, a professional partner in segmented vision fields Compared to traditional detection solutions, Keye adopts AI visual defect detection. When faced with new defect detection needs, there is no need to replace the equipment. Instead, the new defects need to be sampled, labeled, and trained, and the data is uploaded to the cloud platform to develop detection templates. When in use, the corresponding detection templates can be retrieved to ensure stable production.   In addition to having detection functions such as black spots, dirt, burrs, missing materials, and factory model numbers, the Keye AI visual defect detection equipment can also count and process qualified products. At the same time, it is also compatible with defect detection of different sizes of bottle caps, truly achieving efficient work with just one device.     Nowadays, more than 2000 companies around the world use Keye's AI visual defect detection equipment. Behind this achievement is not only the result of Keye's ultimate pursuit of technology, but also a reflection of KeyeTech's strong sense of social responsibility. Get more videos of KeyeTech in Youtube.  

Deploying visual inspection systems has become the first choice for manufacturing enterprises to transform quality inspection and improve product quality. However, enterprises that are not familiar with visual inspection equipment often have certain misunderstandings about the value of visual inspection equipment when choosing. Today, we will summarize several types of problems that enterprises face how to choose visual inspection machines and systems.     Question: If one machine can inspect all products? No, it's not possible. If a company wants to purchase a set of AI visual inspection equipment to test all its products, it is not feasible at this stage.   Although AI visual inspection equipment is compatible, it has a range of requirements for product specifications. Currently, many manufacturing companies have a wide range of products, and products with different materials, shapes, and sizes require different light sources, cameras, and algorithms.     Keye AI image visual detection has a certain degree of compatibility, but the two products differ greatly and it is also difficult to achieve complete compatibility. The visual inspection equipment for bottle caps is compatible with two products with a height difference of no more than one-third and a width difference of no more than half, and there are no irregular caps. Whether the height or width difference is too large, using the same equipment for inspection will affect the final factory quality. Customized solutions based on product characteristics are necessary to ensure the factory quality of the product.     Question: Will setting excessively high testing standards lead to a low yield rate? Yes. Some manufacturing companies, when purchasing vision inspection systems, do not establish inspection requirements based on the actual situation and acceptance standards of the enterprise, but instead use theoretical standards to develop inspection standards. Finally, when debugging and running, it was found that the yield rate was too low, and the visual inspection system was not accurate enough. In fact, this kind of problem belongs to the use of useless ultra-high standards. Enterprises should develop testing standards based on actual situations, increase testing items appropriately to improve testing standards, improve product quality, and maintain market competitiveness.   Question: Is the value of visual inspection systems only reflected in reducing labor costs? No, it's not. A set of AI visual inspection equipment not only saves labor costs, but also reduces the operating costs of enterprises. To improve efficiency, enterprises often choose automated equipment to replace manual labor, which not only improves production capacity and quality, but also reduces operating costs. Keye's AI image visual inspection equipment on a single production line can help enterprises save 3-5 inspection personnel and ensure uniform product quality standards, enhancing customer recognition of the enterprise. In terms of operating costs, Keye's AI image visual detection has played a more significant role. For example, visual inspection of bottles can directly sell qualified products after inspection, and defective products that have been removed can be further processed or reused. The product value can be diversified and maximized.       Question: Can a visual system be used for high production? Suggested to use, but it depends on the business situation of the enterprise. A large output is indeed more suitable for choosing a visual inspection system. From the long-term development strategy of enterprises, manual testing has limited speed, low efficiency, and is more suitable for using automated equipment for testing in large quantities. Although some individual products have low value, using manual visual inspection may result in missed or false inspections. If the products are found in the hands of downstream enterprises and do not meet the standards, they may choose to return them, causing certain losses to the enterprise. Over time, this is not conducive to the long-term development of the enterprise. Therefore, when the production volume of the enterprise's products is large, it is recommended to choose visual inspection equipment. One investment can benefit the enterprise for a lifetime.     Therefore, the choice of AI visual inspection equipment by enterprises is not a direct manifestation of high quality. Only by making reasonable use of AI visual inspection systems to control product quality and effectively eliminate the outflow of defective products can we avoid complaints from end customers and win their trust in the enterprise.    

The new generation visual defect inspection system of Keye is the first algorithm software in China, it adopt AI algorithms to the packaging industry. Currently, the algorithm of this software has been upgraded to KVIS-V16.0. The biggest feature of this detection system is fast defect detection efficiency and high compatibility. With the increasing segmentation of the market, packaging products are becoming more personalized and customized. Each packaging production enterprise has to launch diversified products according to market and customer needs. For example, our company has detected over a hundred types of plastic bottle caps and more styles of bottles. This will make traditional equipment and detection systems face great difficulties in product defect detection.   The biggest feature of the Keye system is that it can be compatible with different styles of products, and multiple products can be tested on the same device. The bottle cap inspection machine can achieve full compatibility with caps of different colors, sizes, and even transparent and opaque lids. The bottle detection machine can be compatible with bottles of different materials, such as PET, PP, PE, PS, etc. The system can detect various defects in products, such as color, structure, classification, etc.     How did we do it?   Firstly, we have customized light sources and imaging systems. Our optical team is led by Professor TANG LING from the University of Science and Technology of China (USTC), and we are also affiliated with the Key Mode Laboratory of Optics at the University of Science and Technology of China, providing strong technical support for our design and research. Currently, we are in a leading position in China. Our company has independently developed various linear, planar, 3D, and intelligent cameras, combined with our self-developed intelligent algorithms, which can achieve system optimization.     The second is the edge computing unit independently developed by our company, which is an embedded high-performance computing platform for industrial scenarios. All algorithms are led and developed by Dr. ZHENG ZHIGANG of China University of Science and Technology (USTC). The rich computer information foundation makes our algorithm team lead the peers in China. A big data platform based on AI deep learning, with built-in multiple algorithm components, can help users quickly build and iterate models.     Finally, our software control system adopts the latest LINUX industrial grade computer system and customized human-machine interaction system, which is stable, efficient, and real-time.

There are currently three inspection methods in the production of plastic packaging containers. The first is traditional manual inspection, which detects defects in the product through eye observation. The second is machine vision inspection, which is based on traditional algorithms. The third is the latest AI algorithm visual inspection system. With the increasing quality requirements for packaging products in the global industry, the efficiency of defect inspection will also become more stringent. Below we will compare several existing testing methods, which will help people find the appropriate testing method to better meet quality requirements and reduce enterprise operating costs.     Due to subjective factors, low efficiency, and susceptibility to fatigue, manual vision inspection cannot guarantee the efficiency and long-term stability. Traditional algorithms vision inspection have many parameters and rely heavily on professional debugging personnel. Poor adaptability, high false detection rate while ensuring detection accuracy, resulting in low detection efficiency. Deep learning AI vision inspection system enables machines to learn the inherent patterns and representation levels of sample data, enabling them to have the ability to analyze, learn, and reason logically like humans. Excellent long-term performance and stability, with efficient detection accuracy.     Human visual inspection has a relatively low recognition rate for colors, which is easily influenced by human psychology and cannot be quantified. Then, machine detection color discrimination can be quantified. For example, human eyes can only recognize 64 grayscale, and machines have strong grayscale recognition ability. Currently, 256 grayscale levels are generally used, and the acquisition system can have grayscale levels such as 10 bit, 12 bit, and 16 bit. The resolution of the eyes is poor, and they cannot view small targets with high resolution. Machines can observe targets at the micrometer level, but the human eye has a slow observation speed. The 0.1 second visual persistence makes it difficult for the human eye to see fast-moving targets clearly. On the other hand, machines have a fast speed, with a shutter time of about 10 microseconds and a high-speed camera frame rate of over 1000. The processor speed is getting faster, and the human eye range is narrow. Visible light devices in the 400nm-750nm range have a wide detection range, ranging from ultraviolet to infrared spectra. Human visual inspection has poor adaptability to the environment, and there are many situations that can cause harm to people. Machine vision inspection has strong adaptability to the environment, and protective devices can also be added. Human eye detection has low accuracy and cannot be quantified. Machine vision has high accuracy and can reach the micrometer level, making it easy to quantify. Relying on human detection also has other subjectivity, psychological influence, and fatigue.     From the above data and analysis, it can be seen that replacing human visual inspection with machine vision inspection will be a trend, especially with the continuous increase in labor costs worldwide. Whether it is from the perspective of production costs, management standards, or detection efficiency, the new generation of AI algorithm visual inspection will be favored by the market. Currently, the visual inspection system supported by the latest generation of AI algorithm by Keye has been increasingly recognized by more customers in domestic and international markets, and has become a leading enterprise in the plastic bottle, cap, printing and other industries. At the same time, it has played a good role in promoting the real landing of artificial intelligence in the packaging inspection market.