You Only Demanufacture Once (YODO): WEEE retrieval using unsupervised learning

Recent developments in robotic demanufacturing raise the potential to increase the cost-efficiency of recycling and recovering resources from Waste of Electrical and Electronic Equipment (WEEE). However, the industrial adoption of robotic demanufacturing for mixed WEEE streams requires tailored instructions for every product model. Considering the large variation in product models, it is not expected to be feasible in the coming decade to rely only on computer vision technologies to define the tailored instructions required for robust and time -efficient robotic demanufacturing. Therefore, the presented research developed a generic retrieval system named You Only Demanufacture Once (YODO) based on content-based image retrieval (CBIR) to identify the product model and retrieve product model-specific demanufacturing instructions. The system compares the vi-sual features represented on a color image of the WEEE with a database of known descriptions representing previously imaged WEEE to find a match or to figure out whether the analyzed product model is new to the system. The performance of YODO is evaluated with a case study for laptop model identification, where a large dataset is created including 4089 images of a representative laptop waste stream. The results demonstrate a top-1 retrieval mean average precision (mAP) of 93.75%. After running YODO on 3600 laptops, the system learned 1079 unique product models, and the presented results show an 85% chance that the next laptop presented to the system is already registered in the database, allowing the retrieval of relevant information for robotic dema-nufacturing. This corroborates that a fast learning rate can be achieved, allowing a YODO system to support the robotic demanufacturing by making prior product-specific learnings available.

Automated summary

Recent developments in robotic demanufacturing have the potential to enhance the efficiency of recycling and resource recovery from WEEE. To achieve industrial adoption, a generic retrieval system called YODO was developed, using content-based image retrieval (CBIR) to identify product models and retrieve model-specific demanufacturing instructions. The system compares visual features of WEEE images with a database to find matches and demonstrates high performance in a laptop model identification case study. YODO showed a top-1 retrieval mean average precision (mAP) of 93.75%, learned 1079 unique product models, and achieved an 85% chance of the next laptop being registered in the database.