DCR-YOLO: An enhanced anti-UAV detection method based on triple collaborative optimization strategy

Unmanned Aerial Vehicles (UAVs) have been widely used in various industries due to their convenience. However, misuse may pose threats to society. Therefore, anti-UAV detection in low-altitude security scenarios is important. The existing detection methods have a core contradiction of being difficult to balance detection accuracy and detection speed, and the detection effectiveness will be obviously reduced in scenarios with complex backgrounds and extremely small UAV targets. To address these issues, this paper proposes an enhanced anti-UAV detection method DCR-YOLO, which improves the YOLO11 model by introducing a triple collaborative optimization strategy. First, the DySample upsampling module is integrated to retain more effective information in the shallow features and make up for the defect of sparse features of small targets. Second, the Convolutional Block Attention Module (CBAM) is incorporated into the detection head to suppress complex backgrounds and enhance the ability to extract features of small targets. Finally, the lightweight ReGhostConv convolution module with channel distillation effect is introduced, which not only can effectively accelerate detection speed, but also can reduce computational complexity and minimize the model size. Experiments on two open-source detection datasets and tracking datasets show that, compared with mainstream detection methods, the proposed method significantly improves the detection accuracy and speed of small target UAVs. Meanwhile, the parameter has been reduced, achieving lightweight. Detection accuracy and speed have been effectively balanced.