A Ship Detection Algorithm for Infrared Images under Hazy Environment based on an Improved YOLOv5 Algorithm

Accurately detecting ships from surveillance images is crucial for intelligent ship traffic surveillance around port waters. To address the issues of low accuracy and capability of small target feature extraction from traditional YOLOv5 object detection algorithms from the infrared images under hazy weather, an improved YOLOv5 algorithm based on Swin Transformer is proposed. To expand the diversity of the original dataset, the improved algorithm considers the characteristics of ship infrared images with strong resistance to cloud and fog interference but blurred image contour features and low contrast, and enhances the dataset based on an atmospheric scattering model. To enhance the algorithm's attention to global features during feature extraction, the backbone network of the improved algorithm uses Swin Transformer to extract ship infrared image features and expands the window view range using a multi-head self-attention mechanism controlled by a sliding window. To enhance the capability of extracting spatial features of dense small targets, a multi-scale feature fusion Path Aggregation Network (PANet) is improved by adding a bottom-up feature sampling module and a coordinate attention (CA) mechanism, in order to capture the position, direction, and cross-channel information of small target ships. To reduce false negatives and false positives, a complete intersection over union loss function (CIoU) is used to calculate the coordinate prediction loss of the original bounding box and combined with the non-maximum suppression algorithm (NMS) to judge and filter candidate boxes in a multi-loop structure to improve the reliability of object detection. Study results show that under certain concentrations of haze, the average recognition accuracy, recall rate, and detection rate of the improved algorithm is 93.73%, 98.10%, and 38.6 frames per second, respectively. Compared with the following algorithms: RetinaNet, Faster R-CNN, YOLOv3 SPP, YOLOv4, YOLOv5, and YOLOv6-N, the average recognition accuracy of the proposed algorithm is improved by 13.90%, 11.53%, 8.41%, 7.21%, 6.20%, and 3.44% respectively; and the average recall rate is improved by 11.81%, 9.67%, 6.29%, 5.53%, 4.87%, and 2.39%, respectively. The proposed Swin-YOLOv5s algorithm has a strong generalization ability for ship target recognition of different sizes and has a high detection accuracy, which helps to improve the surveillance capability of ships around port waters.