A Lightweight YOLO Object Detection Algorithm Based on Bidirectional Multi-Scale Feature Enhancement

This paper proposes a lightweight YOLO object detection algorithm based on bidirectional multi-scale feature enhancement. The problem is that the original YOLOv5 algorithm does not make full use of the relationship between the feature layers, resulting in the loss of target semantic information and a large number of parameters. First, a bidirectional multi-scale feature-enhanced weighted fusion backbone network is constructed to extract target features repeatedly. It enhances the fusion ability of shallow detail features and high-level semantic information to capture richer multi-scale semantic information. Second, the NCA attention module is built and integrated into the feature fusion network to enhance the critical characteristics of the target region. Finally, the Ghost module is used instead of the convolutional blocks in the original network to lighten the model while reducing the network complexity and training difficulty. Experimental results show that the improved YOLOv5 algorithm achieves 78.8% mAP@0.5 for the PASCAL VOC2012 dataset, which is 1.5% higher than the original algorithm, at 62.5 FPS. The number of parameters is also reduced by 43.6%. The mAP@0.5 on the self-made metal foreign object dataset reached 98.4%, at 58.8 FPS, which can meet the requirements of end-device deployment and real-time detection. In this paper, a bi-directional multi-scale feature-enhanced weighted fusion backbone is designed to enhance the fusion capability of shallow features and advanced features. The NCA attention module is designed and embedded into the feature fusion network to enhance the key features in the target region. The Ghost module is used to reduce the network complexity and training difficulty. image