Intelligent identification of fragmented non-magnetic materials for end-of-life refrigerator recycling.

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Abstract

E-waste is a valuable secondary resource containing numerous toxic substances and high-value components. If improperly handled, it will cause severe environmental pollution. Therefore, efficient recycling of this material can reduce environmental pollution. However, after crushing, fine crushing, and magnetic separation, a substantial quantity of fragmented non-magnetic materials with high value, such as copper and aluminum, remain. Refrigerators, as typical e-waste, have a similar composition to fragmented non-magnetic materials. Consequently, this paper focuses on the issues of low efficiency, environmental pollution, and resource waste in sorting fragmented non-magnetic materials from waste refrigerators. This paper constructs a data set of fragmented non-magnetic materials of refrigerators, augments the data set, and identifies fragmented non-magnetic materials of refrigerators using a computer vision-based deep learning method. In this study, YOLOv5s is used as the benchmark model. The CBAM module is added to the backbone to enable intelligent identification and sorting of fragmented non-magnetic materials in refrigerators. The final identification efficiency of waste refrigerators meets the requirements of industrial applications, with an accuracy rate of 98.3%, a recall rate of 96.8%, and an average accuracy of 98%. Based on the similarity of the composition of e-waste fragmented materials, this model sorting method can be applied to sorting additional e-waste fragmented materials. Furthermore, it provides the theoretical foundation for promoting e-waste resourcefulness.