DPCSANet: Dual-Path Convolutional Self-Attention for Small Ship Detection in Optical Remote Sensing Images

Detecting small ships in optical RSIS is challenging. Due to resolution limitations, the texture and edge information of many ship targets are blurred, making feature extraction difficult and thereby reducing detection accuracy. To address this issue, we propose a novel dual-path convolutional self-attention network, DPCSANet, for ship detection. The model first incorporates a dual-path convolutional self-attention module to enhance its ability to extract local and global features and strengthen target features. This module integrates two parallel branches to process features extracted by convolution and attention mechanisms, respectively, thereby mitigating the potential conflicts between local and global information. Additionally, a high-dimensional hybrid spatial pyramid pooling module is introduced into the model to expand the scale range of feature extraction. This enables the model to fully utilize background contextual features to compensate for weak feature representations of the target. To further improve the detection accuracy for small ships, we developed a focal complete intersection over union loss function. This regression loss guides the model to focus on weak targets during training by increasing the contribution of low-accuracy prediction boxes to the loss. Experimental results demonstrate that the proposed method effectively enhances the model’s detection ability for small ships. On the LEVIR-ship, OSSD, and DOTA-ship datasets, DPCANet achieves an average precision improvement of 0.9% to 11.4% over the baseline, outperforming other state-of-the-art object detection models.