Bridge detection method for HSRRSIs based on YOLOv5 with a decoupled head

Abstract

ABSTRACT The different imaging conditions of high spatial resolution remote sensing images (HSRRSIs) tend to cause large differences in the background information of bridges from the images, including problems of difficult detection of multiscale bridges, leakage of small bridges and insufficient detection accuracy for their detection. To address these problems, a YOLOv5 network with a decoupled head for the automatic detection of bridges in HSRRIs is proposed in this paper. First, the problem of inconsistent scale of information fusion of each feature in the feature pyramid network is solved using a weighted bi-directional feature pyramid network (BiFPN). Then, the convolutional block attention module (CBAM) is fused into the three effective feature layers after feature pyramid network processing. The bridge feature information is effectively extracted from the channel and spatial dimensions. Next, the decoupled head is fused in the YOLO Head to separate the classifier and regressor to speed up the network convergence and improve the network detection accuracy simultaneously. Finally, the practical effect is evaluated by calculating the average precision (AP). According to the experimental results, the AP of the proposed method is 98.1%, which is improved by 4.1%∼23.5% compared with other models.