Enhanced YOLOv7 Model for Aerial Drone Detection in Complex Environments

Changlong Fu, Yasu Wu, Xin Jin, Puming Wang, Qian Jiang

Abstract: With the growing popularity of Unmanned Aerial Vehicles (UAVs) in civilian, commercial, and military applications, the need for robust drone detection systems has become increasingly urgent. However, due to the small size of drones when observed from a distance, most traditional machine learning and two-stage deep learning detection methods currently struggle to capture effective feature information of drones in complex image backgrounds, and often fail to meet the requirements for real-time performance. To address this challenge, we have introduced an improved version of the YOLOv7-Tiny model, known as YOLOv7-ADD, which significantly enhances the detection performance of small target drones at various distances and under complex backgrounds. The model integrates the Scylla-IoU (SIoU) loss function to improve the accuracy of bounding box regression and employs the BiFormer attention mechanism, which dynamically focuses on key features of drones within the detection scene, enhancing the model’s recognition capabilities for drones. Furthermore, the introduction of the Diverse Branch Block (DBB) helps the model capture multi-scale features, optimizing the detection effect for drones of various sizes. Extensive experiments on drones dataset have demonstrated the superior performance of YOLOv7-ADD. It achieved a 2.7% improvement in mAP@0.5 and a 1.1% increase in mAP@0.5:0.95, while maintaining high FPS detection performance. This provides an efficient drone detection solution for aerial surveillance. The code is available on https://github.com/FuChanglong/ADD.git.