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Journal of Projectiles, Rockets, Missiles and Guidance >

2022 , Vol. 42 >Issue 4: 17 - 23

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2022.04.004

Improved YOLOv5 Remote Sensing Image Target Detection

  • LI Huihui 1, 2 ,
  • FAN Junfang 1, 2 ,
  • CHEN Qili 2
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  • 1 Beijing Key Laboratory of High Dynamic Navigation Technology, Beijing Information Science and Technology University, Beijing 100192, China
  • 2 School of Automation,Beijing Information Science and Technology University, Beijing 100192, China

Received date: 2022-08-02

  Online published: 2025-05-29

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Abstract

Aiming at the problems of target density, target occlusion and complex background in the process of remote sensing image target recognition, an improved YOLOv5 algorithm is proposed. Firstly, the size of the anchor frame is optimized to make the size scale of each anchor frame more accurate and effectively improve the accuracy of target detection. Secondly, the convolutional attention mechanism is added to pay more attention to the region of interest, suppress useless information and improve the accuracy of target detection. Finally, by adding shallow feature graph to extract learning target features, the recognition accuracy of small targets is increased. Experimental results on data set show that the proposed algorithm has significantly improved recognition accuracy in comparison with YOLOv3, YOLOv4, Faster-RCNN and YOLOv5, and has better robustness in different scenarios. At the same time, the proposed algorithm mAP reaches 97.0%, which is 2.2% higher than original YOLOv5.

Key words： YOLOv5; attention mechanism; small target detection; remote sensing image

Cite this article

LI Huihui , FAN Junfang , CHEN Qili . Improved YOLOv5 Remote Sensing Image Target Detection[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2022 , 42(4) : 17 -23 . DOI: 10.15892/j.cnki.djzdxb.2022.04.004

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