UAV-to-Ground Target Detection Based on YOLO-DSBE

To address the issues of complex background, small target scale, mutual occlusion and high missed detection rate in UAV captured images, this paper proposes a ground target detection algorithm based on YOLO-DSBE.The DC-ELAN and DC-MP modules based on deformable convolution are proposed to adapt to input features of different shapes and sizes, and to improve the network’s ability to parse features in complex backgrounds; A high-resolution multi-scale detection layer is designed to boost the algorithm’s capability in extracting small target features, thereby improving the detection accuracy of minute targets. The algorithm deeply integrates the BiFormer dynamic sparse attention mechanism into the improved feature fusion network, eliminating irrelevant feature information, enhancing the focus on pertinent details, and reducing the missed detection rate. Moreover, the EIoU boundary loss function is incorporated to address the ineffectiveness of the CIoU shape penalty term, enhancing model convergence speed and detection accuracy. The experimental results show that the improved algorithm achieves an average accuracy of 56.1% on the UA-DETRAC dataset, and compared to the original algorithms, and improve by 3.5% and 2.8% respectively on the VisDrone2019 dataset, effectively improving the accuracy of UAV image recognition.