End-of-life vehicles (ELVs) provide a particularly potent source of supply for metals. Hence, the recycling and sorting techniques for ferrous and nonferrous metal scraps from ELVs significantly increase metal resource utilization. However, different kinds of nonferrous metal scraps, such as aluminium (Al) and copper (Cu), are not further automatically classified due to the lack of proper techniques. The purpose of this study is to propose an identification method for different nonferrous metal scraps, facilitate the further separation of nonferrous metal scraps, achieve better management of recycled metal resources and increase sustainability. A convolutional neural network (CNN) and SEEDS (superpixels extracted via energy-driven sampling) were adopted in this study. To build the classifier, 80 training images of randomly chosen Al and Cu scraps were taken, and some practical methods were proposed, including training patch generation with SEEDS, image data augmentation and automatic labelling methods for enormous training data. To obtain more accurate results, SEEDS was also used to optimize the coarse results obtained from the pretrained CNN model. Five indicators were adopted to evaluate the final identification results. Furthermore, 15 test samples concerning different classification environments were tested through the proposed model, and it performed well under all of the employed evaluation indexes, with an average precision of 0.98. The results demonstrate that the proposed model is robust for metal scrap identification, which can be expanded to a complex industrial environment, and it presents new possibilities for highly accurate automatic nonferrous metal scrap classification.

报废车辆（ELV）提供了特别有效的金属供应来源。因此，来自ELV的黑色和有色金属废料的回收和分类技术显着提高了金属资源的利用率。但是，由于缺乏适当的技术，无法进一步自动分类不同种类的有色金属废料，例如铝（Al）和铜（Cu）。这项研究的目的是提出一种识别不同有色金属废料的方法，促进有色金属废料的进一步分离，实现对回收金属资源的更好管理并提高可持续性。本研究采用卷积神经网络（CNN）和SEEDS（通过能量驱动采样提取的超像素）。要构建分类器，拍摄了80张随机选择的Al和Cu废料的训练图像，并提出了一些实用的方法，包括使用SEEDS生成训练补丁，图像数据扩充和用于大量训练数据的自动标记方法。为了获得更准确的结果，SEEDS还用于优化从预训练的CNN模型获得的粗略结果。通过五个指标来评估最终的鉴定结果。此外，通过提出的模型对15个涉及不同分类环境的测试样本进行了测试，在所有采用的评估指标下均表现良好，平均精度为0.98。结果表明，该模型对于金属废料识别具有鲁棒性，可扩展到复杂的工业环境，