GLAF-DETR: Detection Transformer With Global-Local Adaptive Fusion Attention for Infrared Maritime Object Detection

Infrared maritime object detection is a crucial technology for sea surface monitoring in low-light conditions within maritime Internet of Things (IoT) systems. In practical applications, this task faces significant challenges, including diverse target sizes and stringent real-time processing requirements. To address these challenges, a DEtection TRansformer with Global-Local Adaptive Fusion attention for infrared maritime object detection (GLAF-DETR) is proposed. The Global-Local Adaptive Fusion (GLAF) attention mechanism is designed to capture both global contextual information and fine local details of objects. GLAF dynamically adjusts attention across regions by integrating long-range dependencies with short-range positional information, significantly enhancing detection performance for targets of varying sizes in complex maritime environments. In addition, the Dynamic Adaptive Multiscale Feature Fusion (DAMFF) module is proposed to promote cross-channel interaction among multiscale features. Guided by GLAF, DAMFF dynamically fuses these features, further enhancing the accuracy of multiscale object detection. The lightweight HGNetv2-IRLight backbone is designed to minimize network complexity and ensure real-time performance by reducing redundant information while maintaining strong infrared feature extraction. Extensive experiments conducted on an infrared maritime object dataset show that GLAF-DETR surpasses state-of-the-art methods in both detection accuracy and inference speed. It demonstrates outstanding performance, particularly in detecting objects across different scales, offering enhanced accuracy and robustness in challenging maritime scenarios.