Visual inspection machine

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.  

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.    

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.