YOLOv4 Multi-Scale Object Detection Algorithm Based on Adaptive Weighting Module

The direct addition of feature maps in residual block cannot reflect the correlation between features and there is information loss in feature fusion stage. Therefore, an object detection algorithm based on adaptive weighting module is proposed. The algorithm improved the backbone network and neck network of YOLOv4, and an adaptive weighting module is designed to compress the channel of two input feature maps and extract feature information, the spatial weights are then obtained from extracted information, and finally the spatial weights are assigned to the input feature maps to establish correlation between them. In the feature fusion stage, skip connections are added to supplement information loss in feature fusion process. Experimental results show that the proposed algorithm can effectively improve detection accuracy, the mAP values of the proposed algorithm on PASCAL VOC and KITTI datasets are 0.90% and 0.89% higher than that of YOLOv4, respectively.