IRWT-YOLO: A Background Subtraction-Based Method for Anti-Drone Detection

To effectively separate low-contrast weak drone objects from complex backgrounds, the IRWT-YOLO model is proposed, in which image segmentation algorithms are leveraged to reduce background interference. The model integrates object detection and image segmentation, with segmentation utilized to extract additional image information. Furthermore, to address the challenges of limited receptive fields and weak contextual communication in infrared weak object detection, the DCPPA and RCSCAA modules are introduced. The DCPPA module employs dual convolutions to expand the receptive field and enhance feature extraction for weak drone objects. The RCSCAA module incorporates a contextual attention mechanism to capture long-range dependencies and extract multi-scale texture features. Extensive experiments on three datasets demonstrate the superiority of IRWT-YOLO, with a precision improvement of 15.5% on the SIRSTv2 dataset, a recall improvement of 14.5% on the IRSTD-1k dataset, and a 21.0% improvement in $mA{P}_{50-95}$ on the 3rd Anti-UAV dataset compared to YOLOv8. These results highlight the model’s robustness and effectiveness in detecting weak objects under complex infrared conditions.