UAV Image Object Detection Based on Attention Mechanism and Dilated Convolution

Abstract

Existing algorithms for unmanned aerial vehicle (UAV) image object detection often face challenges such as low detection accuracy for small objects and missed detections of multi-scale objects. To address these issues, this paper proposes a UAV image object detection algorithm that integrates a channel attention mechanism with parallel-structured dilated convolution feature fusion. To enhance the algorithms feature representation capabilities in terms of channel attention and receptive field, the ResNet50 backbone is redesigned by incorporating the Squeeze-and-Excitation Network (SENet) and a Parallel-Structured Dilated Convolution Feature Fusion Network (PSDCFFN). Additionally, Region of Interest (ROI) Align is employed, and the Region Proposal Network (RPN) anchor sizes are optimized using K-Means clustering to minimize coordinate deviations during object regression. Experimental results demonstrate that the proposed algorithm significantly improves object detection accuracy in UAV images. On the RSOD-Dataset and a custom UAV image dataset, the mean Average Precision (mAP) reaches 92.52% and 98.07%, respectively.