Research on the process of small sample non-ferrous metal recognition and separation based on deep learning.

• A small sample non-ferrous metal target detection model was proposed. • Improved YOLOv3 model was compared with traditional image recognition model. • Recognition accuracy of aluminum and copper scraps was 95.3% and 91.4%. • Separation accuracy and purity are all more than 90% in the optimal condition. Consumption of copper and aluminum has increased significantly in recent years; therefore, recycling these elements from the end-of-life vehicles (ELVs) will be of great economic value and social benefit. However, the separation of non-ferrous materials is difficult because of their different sources, various shapes and sizes, and complex surface conditions. In experimental study on the separation of these materials, few non-ferrous metal scraps can be used. To address these limitations, a traditional image recognition model and a small sample multi-target detection model (which can detect multiple targets simultaneously) based on deep learning and transfer learning were used to identify non-ferrous materials. The improved third version of You Only Look Once (YOLOv3) multi-target detection model using data augmentation, the loss function of focal loss, and a method of adjusting the threshold of Intersection over Union (IOU) between candidate bound and ground truth bound has superior target detection performance than methods. We obtained a 95.3% and 91.4% accuracy in identifying aluminum and copper scraps, respectively, and an operation speed of 18 FPS, meeting the real-time requirements of a sorting system. By using the improved YOLOv3 multi-target detection algorithm and equipment operation parameters selected, the accuracy and purity of the separation system exceeded 90%, meeting the needs of actual production. [ABSTRACT FROM AUTHOR]