YOLO-D: Dual-Branch Infrared Distant Target Detection Based on Multi-level Weighted Feature Fusion

Infrared distant target detection is crucial in border patrol, traffic management, and maritime search and rescue operations due to its adaptability to environmental factors. However, in order to implement infrared distant target detection for aerial patrols using Unmanned Aerial Vehicles (UAVs), the challenges such as low signal-to-clutter ratio (SCR), limited contrast, and small imaging area have to be addressed. To this end, the paper presents a dual-branch infrared distant target detection model. To be specific, the model incorporates a contour feature extraction branch to improve the network’s ability in recognizing distant targets and a multi-level weighted feature fusion method that combines contour features with their original counterparts to enhance target representation. The proposed model is evaluated using the High-altitude Infrared Thermal Dataset for Unmanned Aerial Vehicles (HIT-UAV), which includes persons, cars, and bicycles as detection targets at altitudes ranging from 60 to 130 m. Experimental results show that, in comparison with the state-of-the-art models, our model improves the Average Precision (AP) of persons, bicycles, and cars by 2%, 2.21%, and 0.39% on average, respectively, and improves the mean Average Precision (mAP) of all categories by 1.53%.