Forestry pest detection method based on improved YOLOv8n

Chen Wanzhi; Yuan Hang

doi:10.12171/j.1000-1522.20240326

Journal of Beijing Forestry University > 2025 > 47(2): 119-131. > DOI: 10.12171/j.1000-1522.20240326

Chen Wanzhi, Yuan Hang. Forestry pest detection method based on improved YOLOv8n[J]. Journal of Beijing Forestry University, 2025, 47(2): 119-131. DOI: 10.12171/j.1000-1522.20240326

Citation:

Chen Wanzhi, Yuan Hang. Forestry pest detection method based on improved YOLOv8n[J]. Journal of Beijing Forestry University, 2025, 47(2): 119-131. DOI: 10.12171/j.1000-1522.20240326

Citation:

Chen Wanzhi, Yuan Hang. Forestry pest detection method based on improved YOLOv8n[J]. Journal of Beijing Forestry University, 2025, 47(2): 119-131. DOI: 10.12171/j.1000-1522.20240326

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Forestry pest detection method based on improved YOLOv8n

School of Software, Liaoning Technical University, Huludao 125105, Liaoning, China

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Received Date: October 06, 2024
Revised Date: November 13, 2024
Accepted Date: December 06, 2024
Available Online: December 10, 2024

Graphical Abstract

Abstract

Abstract

Objective
In response to the problem of slow speed, narrow categories, and poor detection of small-target pests in existing forestry pest detection methods, a forestry pest detection method based on the improved YOLOv8n was proposed.
Method
Firstly, an efficient multi-scale cascade attention feature extraction network EfficientViT was adopted as the backbone of improved model to reduce computational complexity and enhance detection speed. Secondly, a multi-scale adaptive feature fusion module DA-C2F was constructed to improve the model’s ability to focus on and accurately identify pest targets in complex backgrounds. Additionally, a newly added small-object detection head XSH further enhanced the detection capability for small pest objects. Finally, a minimum point distance IoU loss function MPDIoU was implemented as the bounding box loss for the model, accelerating convergence speed and further improving the accuracy of pest target localization.
Result
The improved model achieved a detection precision of 98.6%, recall of 95.7%, mean average precision of 98.3%, mean average precision at different thresholds (mAP50-95) of 85.6%, and an F₁-score of 0.979. These metrics represented improvements of 3.9, 2.6, 2.8, 2.5 percentage points and 4.4%, respectively over the original model. The detection speed reached 95 frames per second with an improvement of 15.9%, also surpassed that of the lighter-weight model. Furthermore, in comparison with other detection models, the improved model demonstrated an increase of 11.2 percentage points in detection precision for moth pests, and exhibited superior overall performance in detection of two independent moth pest species.
Conclusion
The proposed method has higher detection accuracy and faster detection speed for forestry pests, better detection accuracy for multiple categories of pests, and better generalization ability of the improved model.

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References (38)

References

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Forestry pest detection method based on improved YOLOv8n