This project collects domestic and foreign cases of using image recognition to assist waste recycling and processing, and grasps the core technology and application aspects; reviews the current six major procedures of the audit and certification process, and uses image information to identify and count waste products and control the flow of waste products. Purpose, the evaluation technology is suitable for the dismantling process, and can combine the relevant information flow with the existing forms, which can reduce the workload of the work. This project examines the CCTV images of the existing dismantling and processing areas in China. Considering the image recognition conditions, it has been assessed that the existing CCTV images are not suitable for AI identification and counting. However, some of the information can be used to monitor the flow of waste products through angle and position adjustment.This project collects high-resolution images for dismantling and processing of LCD TVs, air-conditioners, refrigerators, and washing machines from 5 subsidized organizations, and completes 1,000 images of waste products and 500 images of spindle components. Set the image recognition model learning data set.This project uses the YOLOv4 object detection algorithm as the core to build an image recognition model. The average accuracy rate (mAP) of the model is 97.7%, and then verified with real factory images. The model recall and precision rate reach 90%, showing that the image recognition technology can detect Measure and identify waste electronic and electrical products with complex materials and appearances.Combining an image model with certain recognition capabilities with an object tracking algorithm and counting line auxiliary logic to construct a model counting function. After evaluating the on-site operation mode, the dismantling of waste refrigerators in the assembly line is more feasible for technical introduction. Therefore, the waste refrigerators are the test targets for waste counting. After the AI misjudgment problems are summarized and adjusted, the model counts and audits. The difference in the amount of certification is 2 sets and the counting is feasible in certain condition. The evaluation result of the recognition rate of this project shows that the image recognition technology can identify, classify, track and count the number of waste products, combined with the aforementioned application-oriented supporting measures for the development of technical and administrative aspects, including the identification and counting principles, and the application of software and hardware. Specifications and identification information transmission structure, etc.

Image recognition technology, Waste appliances, Auditing and certification