Vehicle object detection based on improved YOLOv5 method

Abstract:

In view of the problems of low detection accuracy, poor real-time and robustness of existing target detection algorithms in vehicle target detection in autonomous driving fields, a vehicle target detection method based on YOLOv5 is proposed. With the framework of YOLOv5s network model, a one-shot aggregation (OSA) module is introduced to optimize the backbone network structure and improve the network feature extraction capability. Non-local attention mechanism is used for feature enhancement. At the same time, the weighted non--maximum suppression method is used to filter the detection frame. The experimental results show that compared with the original YOLOv5s model, the mAP of the improved network model is improved by 3%, and the AP of different target detection classes is improved, and the detection speed meets the real-time requirements. For dense vehicles and under different illumination conditions, vehicle target detection can be better achieved.