SRCANet: Stacked Residual Coordinate Attention Network for Infrared Ship Detection

The inability of conventional algorithms to detect infrared (IR) ship targets in complex scenes led to the development of detection methods based on convolutional neural networks (CNNs). In this study, we propose a CNN-based stacked residual coordinate attention network (SRCANet) for detecting IR ship targets. Three-directional stacked interaction modules and a full-scale skip connection feature fusion scheme are introduced. The proposed network maintains and integrates sufficient contextual information of IR ship targets and obtains clear target boundary information. A cascaded residual coordinate attention module (CRCAM) is designed as the basic node in the SRCANet. Additionally, a residual coordinate attention module (RCAM) is introduced, which combines a two-dimensional convolution layer with batch normalisation and rectified linear unit (CBR), a coordination attention module, and a residual connection. The RCAM enhances the input feature map and improves the representability of objects of interest. The CRCAM comprises several cascading RCAMs that deepen the feature extraction layers. Furthermore, because there is no publicly available IR ship target dataset for segmentation, pixel-level annotations are performed on a set of IR ship target images and released as a single-frame IR ship detection (SISD) dataset. Extensive experiments were conducted on the SISD dataset and the widely used single-frame IR small target dataset to demonstrate the superiority of the proposed method. The results indicate that the SRCANet outperforms the state-of-the-art models, and it is more robust when target texture information is lacking. The SISD dataset is available at https://github.com/echo-sky/SISD.