Improved YOLOv5 UAV Target Detection Algorithm by Fused Attention Mechanism
Authors: 
Yan He, Yanni Zhao, Hongfei NieAuthors Info & Claims
Yan He, Yanni Zhao, Hongfei NieAuthors Info & ClaimsAbstract
This paper proposes a modified YOLOv5 UAV target detection algorithm for the low detection accuracy caused by the dense target distribution and too small size in the UAV image. Firstly, the coordinate attention mechanism (Coordinate Attention, CA) is introduced in the backbone network CSPDarknet53 to enhance the feature extraction capability of the network; secondly, the multi-size feature pyramid network is designed to introduce a larger resolution feature map for feature fusion and prediction, and to improve the accuracy of small target detection. Experiments on the VisDrone2021 dataset, the results show that the average detection accuracy (Mean Average Precision, mAP) of the improved YOLOv5 algorithm reached 43.0%, 5.8 percentage points higher than the original algorithm, which fully proves the high efficiency of the proposed improved algorithm on the ground target detection of the UAV.
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Published In
March 2023
598 pages
ISBN:9781450399449
DOI:10.1145/3590003
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Association for Computing Machinery
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Published: 29 May 2023
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CACML 2023
CACML 2023: 2023 2nd Asia Conference on Algorithms, Computing and Machine Learning
March 17 - 19, 2023
Shanghai, China
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CACML '23 Paper Acceptance Rate 93 of 241 submissions, 39%;
Overall Acceptance Rate 93 of 241 submissions, 39%
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