Pine wilt disease detection method based on improved YOLOv5

Mengwei Zhao; Rongshuang Fan; Jiawei Yin; Jun Zheng; Wenbo Bao

doi:10.1117/12.3035662

10 July 2024 Pine wilt disease detection method based on improved YOLOv5

Mengwei Zhao, Rongshuang Fan, Jiawei Yin, Jun Zheng, Wenbo Bao

Proceedings Volume 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024); 132231Q (2024) https://doi.org/10.1117/12.3035662
Event: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2024), 2024, Wuhan, China

Abstract

Pine Wilt Disease (PWD) poses a serious threat to forest resources. This study proposes an improved YOLOv5 algorithm aimed at the remote sensing image detection of this disease. By introducing a multi-scale detection mechanism and attention mechanism, the improved model significantly enhances the recognition capability for small targets. Experimental results show that the accuracy of the improved model reaches 89.84%, which is a significant improvement over traditional methods and the original YOLOv5. This method provides effective technical support for the rapid and accurate detection of PWD, which is of great significance for forest disease prevention and control.

Citation Download Citation

Mengwei Zhao, Rongshuang Fan, Jiawei Yin, Jun Zheng, and Wenbo Bao "Pine wilt disease detection method based on improved YOLOv5", Proc. SPIE 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024), 132231Q (10 July 2024); https://doi.org/10.1117/12.3035662

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