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The rural-urban fringe is an important part of urban construction. Due to the difficulty of deploying effective detection equipment, the night supervision of vehicle targets in this area has been a difficult problem for urban management. Multi-moving target detection based on infrared night vision images of UAV platform provides an intelligent path to solve this problem: A multi-moving target recognition method based on improved YOLOv5 under infrared night vision conditions analyzed the characteristics of traffic objects and the impact of vehicle parking on road infrared radiation, etc. CBAM attention mechanism was introduced to extract and integrate spatial and channel information to enhance the expression ability of the network to the target. Combining the advantages of Efficient IOU Loss and Focal Loss, the EIoU-Focal loss function was used to replace CIoU loss function, which solved the disadvantages of sample imbalance, low resolution of infrared image, large noise interference and low contrast between target and background, and improved the detection accuracy. By adding DCN to dynamically adjust the shape of the convolution kernel, it can adapt to the deformation of the object in the image, and reduce the recognition influence caused by irregular shape and many changes. Finally, experiments and data comparisons indicate that the improved network based on YOLOv5 achieves higher recognition results and accuracy.