A small object detection network for remote sensing based on CS-PANet and DSAN

The proportion of small objects in remote sensing images is relatively small, which is prone to feature loss or interference from the surrounding complex background in the detection process. To solve this problem, a remote sensing small object detection network (CD-YOLOX) based on enhanceds’ feature pyramid network (CS-PANet) and global local combine module (DSAN) based on YOLOX is proposed. Firstly, to solve the problem of small object feature loss and surrounding complex background interference caused by multiple convolution and feature stacking operations in PANet, CS-PANet is proposed. This method improves the network’s focus on effective feature channels for small objects by using channel attention at the input of PANet, meanwhile, the input that passes the channel attention is connected to the output of PANet across layers, which makes the PANet retain the richer original information of small objects. Secondly, to further reduce the interference of the surrounding complex background on the small objects, a DSAN module consisting of self-attention mechanism, dilated convolution and residual connection is proposed before network prediction. This module combines self-attention mechanism with dilated convolution so that the network focuses on the global feature region of the small object in the feature map while effectively complementing the local context of this region, and preserving the original information through residual connection. Finally, the effectiveness of the method is verified using the remote sensing dataset NWPU VHR-10 and the general datasets KITTI and PASCAL VOC. The experiment shows that the method improves the detection accuracy by 5.56% in the NWPU VHR-10 dataset and by 1.93% and 2.51% in the KITTI and PASCAL VOC datasets respectively compared to the original network, which fully verifies the effectiveness of the method for detecting the small objects of remote sensing and the ability of the method to detect general purpose objects.