Abstract
Target detection from the perspective of UAV has great potential in the field of urban regulation, limited by the dense small targets, severe environmental obstructions, camera shake, and changes in lighting conditions in the aerial view of drones, the existing object detection algorithms cannot effectively undertake this task. This paper introduces two lightweight feature extraction modules based on YOLOv5, which are C3-Faster with PConv and COT3 with transformer structure. Meanwhile, an extra small detection head is added to the output layer. These approaches enhance accuracy while maintaining the advantages of being lightweight and easy to deploy. The ablation experiments and comparative experiments are designed to verify the effectiveness of these modules. The algorithm presented in this paper can be deployed into embedded systems of small UAVs to assist UAVs in completing various regulatory tasks in complex urban scenarios.
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