Research on Abnormal Ship Brightness Temperature Detection Based on Infrared Image Edge-Enhanced Segmentation Network

Infrared imaging is based on thermal radiation and does not rely on visible light, allowing for it to operate normally at night and in low-light conditions. This characteristic is beneficial for regulatory authorities to monitor ships. Existing infrared image segmentation methods face challenges such as the absence of color information, blurred edges, weak high-frequency details, and low contrast due to the imaging principles. Consequently, the segmentation accuracy for small-sized ship targets and edges is low, influenced by the indistinct features of infrared images and the weak difference between the background and targets. To address these issues, this paper proposes an infrared image ship segmentation algorithm called the Infrared Image Edge-Enhanced Segmentation Network (IERNet) to extract ship temperature information. By using pseudo-color infrared images, the sensitivity to edges is enhanced, improving the edge features of ships in infrared images. The Sobel operator is used to obtain edge feature maps, and the Convolutional Block Attention Module (CBAM) extracts key feature information. In the Fusion Unit, edge features guide the extraction of infrared ship features in the backbone network, resulting in feature maps rich in edge information. Finally, a specialized loss function with edge weights supervises the fusion features. An eXtreme Gradient Boosting (XGBoost) machine learning model is then established to predict the ship image brightness temperature threshold, using engine brightness threshold, water area brightness threshold, boundary brightness threshold, and temperature gradient as predictive elements. In terms of image segmentation, our algorithm achieves a segmentation performance of 89.17% mIoU. Regarding the XGBoost model’s performance, it achieves high goodness of fit and small error values on both the training and testing sets, demonstrating its good performance in predicting ship temperature. The model achieves over 70% goodness of fit, and the RMSE values for both models are 3.472, indicating minimal errors. Statistical analysis reveals that the proportion of ship temperature differences predicted by the XGBoost model exceeding 2 is less than 0.020%. The proposed temperature detection method offers higher accuracy and versatility, contributing to more efficient detection of abnormal ship temperatures at night.