Improved forest pest detection based on YOLOv8

Hui Jun Xiong; Xiao Qin Chen; Hong Xie

doi:10.1117/12.3049799

8 November 2024 Improved forest pest detection based on YOLOv8

Hui Jun Xiong, Xiao Qin Chen, Hong Xie

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134163H (2024) https://doi.org/10.1117/12.3049799
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Climate change-induced fluctuations in temperature and humidity create favorable conditions for the reproduction and spread of forest pests. Additionally, this unstable climate environment weakens the natural resistance of trees, making forest areas more susceptible to pest infestations. Detecting these pests is a challenging task. The diversity in the shape, color, and size of pests, along with variations in lighting conditions, significantly impacts the accuracy of detection results. To effectively tackle these issues, this paper enhances YOLOv8s by integrating a Dilation-wise Residual (DWR) module in the backbone to capture high-level multi-scale contextual information, and a Simple Inverted Residual (SIR) module to extract features from the lower layers of the network, thereby improving the feature extraction efficiency for real-time semantic segmentation. These improvements not only enhance the precision of detection but also significantly boost the computational efficiency of the model, making it more suitable for practical applications. Tests on a forestry pest dataset show that our model achieves superior performance, reaching an 0.815 AP detection accuracy, surpassing YOLOv8n by 3.4% AP.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hui Jun Xiong, Xiao Qin Chen, and Hong Xie "Improved forest pest detection based on YOLOv8", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134163H (8 November 2024); https://doi.org/10.1117/12.3049799

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KEYWORDS

Object detection

Feature extraction

Convolution

Education and training

Climate change

Visualization

Agriculture

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