An improved algorithm is proposed to address the problem that yolov5 detects UAV aerial images with poor monitoring performance for low light background targets. Firstly, data normalization is performed on the target dataset visdrone2019 to improve the detection effect. Then the dynamic convolution kernel with Mish activation function and the C3_DSConv module using distrib‐ uted offset convolution to replace the C3 block are introduced, and the above two convolution structures are fused into the yolov5 network; the BiFormer attention mechanism is embedded to improve the accuracy of small target detection. In summary, the MODByolov5 model is finally obtained, and the experimental results prove that the model's mAP and recall are both improved, and the ac‐ curacy of detecting vehicles in shadows and dark environments is significantly increased, and the FPS is high, which ensures that the model can still be used for rapid detection or real-time monitoring.