Target Detection Algorithm for Remote Sensing Images with Multi-Scale Information Enhancement

Feature extraction from remote sensing images with complex backgrounds is challenging, and he accuracy is low due to the high density of small targets and significant scale variations. To address these challenges, this paper proposes a multi-scale information-enhanced target detection algorithm based on YOLOv5s: Deep Learning YOLO(DL-YOLO). First, the improved algorithm employs cavity convolutional fast spatial pyramid pooling designed based on Spatial Pyramid Pooling-Fast (SPPF) at the top of the backbone network. This improves the feature extraction capability of the network by fusing the detailed information of the multi-scale targets with the semantic information through the Receptive Field Enhancement Block (RFEB). Second, the improved algorithm incorporates a Lightweight and Efficient Detection Head (LEDH), which is based on the Decoupling Head (DH) of YOLOv6. The original detection head is replaced with the LEDH, which features a lightweight cavity Global Depth Convolution (GDConv) module, to improve the correlation learning of classification and regression tasks. The LEDH also employs lightweight convolution for lightweighting purposes, which enhances the target detection accuracy at different scales and reduces the number of decoupling head parameters. The results of the experiment on the DIOR dataset demonstrate that the proposed DL-YOLO algorithm increases precision, recall, mAP@0.5, and mAP by 1.6, 2.1, 2.1, and 4.7 percentage points, respectively, compared with YOLOv5s. The all-around score of the proposed algorithm surpasses those of several current exceptional target detection algorithms; hence, it is feasible for detecting targets in remote sensing images at multiple scales.