Instance segmentation algorithm for sorting dismantling components of end-of-life vehicles - Fan, Binbin; Qin, Xunpeng; Wu, Qiang; Fu, Jie; Hu, Zhongliang; Wang, Zhe (Engineering applications of artificial intelligence, 2024)
In the recycling and dismantling procedures of End-of-Life Vehicles, the diverse thicknesses of disassembled parts necessitate specialized recycling processes, underscoring the imperative for efficient classification and recycling methods. Presently, the classification of disassembled components from scrapped vehicles relies heavily on manual visual inspection and caliper measurements, resulting in inefficiency and low accuracy. To overcome the challenges associated with thickness classification for disassembled components of End-of-Life Vehicles, propose a paradigm shift by introducing an intelligent grading strategy. This approach aims to significantly improve efficiency and accuracy in the classification process, offering a more effective solution for the recycling of dismantled parts. In response to the inefficiencies, low accuracy, and safety concerns associated with manual sorting, propose a deep learning model, incorporating an attention mechanism, for accomplishing instance segmentation tasks of End-of-Life Vehicles components. The model introduces a global attention mechanism into the backbone network for feature extraction, which is further integrated into the instance segmentation branch, resulting in a significant enhancement of instance segmentation performance. The model is trained and optimized using a dataset collected from an End-of-Life Vehicles dismantling facility in China. Experimental results show that when the intersection over union is 0.5, the mean Average Precision of the model in target detection and instance segmentation reaches 93.2% and 92.1% respectively., and 77.2% and 72.4% in the intersection over union is 0.5–0.95. In comparison to the unmodified baseline model, the model exhibits improvements of 1.7%, 2%, 5.8%, and 3.1% across evaluation metrics. When contrasted with traditional manual sorting methods, the proposed model exhibits pronounced advantages in terms of accuracy and fairness. In conclusion, the model effectively addresses the challenge of intelligent sorting of dismantled components from End-of-Life Vehicles during the dismantling process.