Abstract
Aiming at the problem of poor detection results due to the large difference in scale of detection targets and numerous small targets in remote sensing images, a remote sensing target detection algorithm DCB-YOLO based on atrous convolution and multi-scale feature fusion is proposed. Based on the YOLOv5 algorithm, the algorithm designs a bidirectional feature pyramid structure (MDCB-BiFPN) with multi-branch hole convolution. The MDCB-BiFPN structure can better extract and fuse features of different scales, thereby improving the model's ability to detect multi-scale remote sensing targets. The experimental results on the remote sensing public dataset DIOR show that the DCB-YOLO algorithm can better accurately identify remote sensing targets at different scales.
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