ReM-YOLO: A New Lightweight Vehicle Parts Target Detection Algorithm

Abstract

In the scene of equipment maintenance, the equipment parts target detection technology can provide technical support for maintenance personnel, and lightweight algorithms based on deep learning have been much concerned, which have the advantages of strong feature extraction and short delay time. YOLOv7 is considered as a new algorithm in the YOLO series, which offers many optimized modules to improve target detection abilities. However, YOLOv7 has problems such as huge amount of computation and parameters, serious memory consumption, and the over-optimized structure. In this paper, a lightweight algorithm ReM-YOLO based on YOLOv7 is proposed to improve the network structure. YOLOv7 is improved by adding C3 blocks, MobileOne blocks and Rep-DSC blocks to reduce the model size while maintaining high precision, and a non-parameter SimAM attention module is employed to further improve the detection accuracy. Compared to YOLOv7, the ReM-YOLO has better improvements in precision and recall, and the model size is reduced by 1/3 size of YOLOv7. It has been observed that experimental tests are carried out on our dataset of vehicle engine components with the high accuracy rate of 96.2%. The improved algorithm helps further experiments about model compression effectively.