LEANet: lightweight and efficient UAV infrared target detection network based on multi-scale attention

Abstract In response to the challenges of insufficient feature information, strong background interference and limited hardware resources faced by object detection in UAV infrared images, we propose a lightweight and efficient UAV infrared target detection network based on multi-scale attention (LEANet). First, a perception-enhanced lightweight convolution module based on C3K2 is designed to boost the backbone network’s capability in feature extraction and reduce the model’s parameter count. Then, an efficient multi-scale attention module is to capture long-range dependencies between targets and contextual information, thus enhancing focus on potential targets while suppressing background noise in complex scenes. Next, an efficient lightweight feature pyramid network is designed, which not only incorporates a dynamic upsampling method to tackle the blurring and detail loss in infrared small targets caused by traditional upsampling methods but also introduces group sparse convolution to improve model computational efficiency and a lightweight feature extraction module to diminish the model’s parameters. Finally, the Shape-IoU loss function is utilized to boost the model’s adaptability to diverse target shapes and sizes, thereby boosting the detection accuracy of diverse targets observed via UAV infrared sensors. Experimental results on the public HIT-UAV dataset and the self-built X-UAV dataset show that LEANet achieves the optimality mAP50 with minimal increases in computational effort and inference time compared to the lightweight models in the Yolo series. Notably, LEANet reduces model parameters by 13.6% relative to YOLOV5n, the smallest model in the YOLO series.