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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.    

Appearance Defect Detection of Bottle Body The appearance defect inspection of the bottle body is a common problem in daily production. For manufacturers in industries such as pharmaceuticals, dairy products, alcoholic beverages, seasonings, and daily chemical products, due to the increasing demand for product packaging refinement, quality, and continuous batch production, the market has put forward higher standards for bottle factory quality testing. The emergence of machine vision inspection machines based on AI algorithms, it has become a new tool to assist enterprises in efficient production.   Vision Defect Inspection Solution of Bottle Body Based on the theory of computer vision and pattern recognition, and equipped with the AI edge computing unit developed by KeyeTech, it fully meets the computing power support under the requirements of high-definition and high-speed. It has the advantages of high computing power, high stability, and low power consumption, and optimizes the problem of computing power distribution in multi camera collaborative processing.   Software & Hardware Platform (1) Self developed high-precision CCD/CMOS industrial camera (camera+lens) (2) Self developed surface light sources, circular light sources, and other LED light sources (3) Semi supervised learning mode (4) Self developed AI edge computing unit, a high-performance embedded computing platform for industrial scenarios (5) Building an AI cloud training platform independently     Detection Contents Bottle defects: black spots, color difference, impurities, threads, lifting rings, notches, residues, burrs, bubbles, holes, uneven thickness, deformation, size, spray code, trademark, mold number, etc   Bottle material: PET, PE, PP, HDPE, PC, etc   Widely Applications KeyeTech AI visual defect inspection technology is widely used in industries such as pharmaceuticals, dairy products, seasonings, alcoholic beverages, and daily chemicals. Solution Advantages High efficiency Self developed AI edge computing unit has faster speed, lower power consumption, stronger continuous computing capability, no power failure at high temperature, and stable operation.   High Integration Integrating light, machinery, electronics, computing, and software, we have built an AI platform with higher integration, faster computation, and stronger processing capabilities.   Strong data compatibility The semi supervised learning mode effectively solves the problem of small data samples and difficult labeling.   High flexibility Support fast switching of detection scenarios   Easy to operate 3 minutes to get started, with 7X24 hours of remote operation and maintenance support.      

AI algorithm - the core driving force of machine vision system Machine vision is an important branch technology in the field of artificial intelligence, and its key lies in implanting "human eyes and brain" into machines. AI algorithms provide the internal core driving force for machine vision inspection system. Regardless of the application in any scenario, machine vision needs to think, judge, and execute actions like humans. AI algorithms provide underlying data logic support for machine vision, including difficulties in target object judgment execution, complex and variable environments, feature learning and recognition, real-time performance and computing resources, and model generalization ability. With continuous increase in research and development investment and upgrading of algorithm systems, these difficulties have been gradually overcome.   Continuously Expanding Application Scenarios from Traditional to Innovation With the support of AI algorithms, the hidden properties of machine vision are released, and the application scenarios continue to expand. From the underlying logic, technology to application, AI algorithms greatly empower the intelligent application of machine vision inspection system in images, quickly establish classification models with small samples, reduce data collection and calculation costs, and improve image recognition accuracy.     At first, KeyeTech mainly applied machine vision in the field of plastic packaging defect detection. Currently, a deep collaboration among multiple industries has been formed, integrating the entire industry chain of light, machinery, electricity, computing, and software.   Through project practice in packaging containers, electronics, new energy, medicine, textiles, food and other fields, we aim to continuously expand the application scenarios of "Keye AI Machine Vision" and transform AI into true productivity.     From plastic packaging visual inspection, to visual empowerment of new energy, and to the creation of a "three in one inspection solution" for glass wine bottles, all are the fusion of "Keye machine vision". In the future, KeyeTech will also achieve comprehensive breakthroughs in AI machine vision application scenarios.

Machine vision inspection is a rapidly developing branch of artificial intelligence (AI). According to the definitions of machine vision by the Machine Vision Division of the Society of Manufacturing Engineers (SME) and the Automation Vision Division of the Robotics Industry Association (RIA), machine vision is a device that automatically receives and processes an image of a real object through optical devices and non-contact sensors to obtain the required information or to control robot motion.   Simply speaking, machine vision is using machines instead of human eyes. Machine vision simulates the eyes for image acquisition, extracts information through image recognition and processing, and finally completes the operation through the execution device.   Traditional machine vision inspection technology requires representing data as a set of features or inputting them into a prediction model to obtain prediction results. This requires completing specific actions, making it difficult to adapt to future flexible production needs, especially in scenarios where defect types are complex, subtle, and background noise is becoming increasingly difficult to apply.     After being equipped with AI deep learning function, machine vision converts the original data features into a higher-level and more abstract feature representation through multi-step feature transformation, and further inputs it into the prediction function to obtain the final result.     Machine vision based on deep learning can combine the efficiency of machine vision with the flexibility of human vision in an ideal state, thus completing detection in increasingly complex environments, especially when involving deviations or extreme environments, meeting the stringent requirements of downstream for defect accuracy and universality.  

High precision AI algorithm, meeting hundreds of subdivision scenarios Product appearance defect Inspection Mainly used for product appearance defect visual inspection system in the industrial field, including black spots, scratches, damages, foreign objects, discoloration, deformation, specifications, third phase, EAN product barcodes, etc Application scope Machine vision inspection for bottle cap bottle body, bottle preform, cup packaging, filling, spray coding (missing, blurry, not spray coded), alcohol packaging, bowl, pharmaceutical packaging; Non woven fabric, earth cloth, denim fabric, embroidered fabric; Capacitors, lighting displays, electronic components, shielding covers, connectors; COVID-19 self test kit, gloves, measuring cup, syringe, pregnancy test stick, etc. Grain quality analysis Automated, information-based, and intelligent solutions for incomplete grain analysis The KVS-G series grain quality sorting machine consists of a visual system, software system, and other module structures. When the grain enters the camera's field of view, it is photographed and the characteristics of a complete grain are comprehensively obtained through registration algorithms. Artificial intelligence algorithms are used for attribute recognition to determine whether there are problems such as disease spots, mold, sprouting, damage, and insect erosion. Application scope Quality analysis of stable agricultural products such as rice, corn, wheat, melon seeds, pine nuts, almond wood, coffee beans, etc., classified and counted according to national standards, separated and weighed (optional) for different types.   Resource recycling Based on AI algorithm classification, template matching algorithm, data preprocessing algorithm and other technologies, Keye Technology can accurately identify the materials that customers need to classify from mixed, stacked, adhesive, damaged, and dense materials, and sort them accurately. Application scope Classification of construction waste, kitchen waste, industrial waste, recycled plastics, household waste, etc.