UAV Image Object Detection Based on Attention Mechanism and Dilated Convolution

Shijie Lyu

doi:10.54254/2755-2721/2025.24679

Applied and Computational EngineeringOpen access

UAV Image Object Detection Based on Attention Mechanism and Dilated Convolution

Research Article

Open Access

UAV Image Object Detection Based on Attention Mechanism and Dilated Convolution

Shijie Lyu ^1*

¹ Georgia Institute of Technology, North Avenue, Atlanta, GA, 30332, United States of America

^*Corresponding author: slyu41@gatech.edu

Published on 4 July 2025

ACE Vol.173

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-231-7

ISBN (Online): 978-1-80590-232-4

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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 algorithm’s 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.

Keywords:

UAV Image, Faster R-CNN, Attention Mechanism, Dilated Convolution, Feature Fusion, Object Detection

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