YOLOv8s-SLS: a mulberry leaves pest detection model integrating lightweight and multiscale

Zhonghang Liu; Hankui Liu

doi:10.1117/12.3051355

18 November 2024 YOLOv8s-SLS: a mulberry leaves pest detection model integrating lightweight and multiscale

Zhonghang Liu, Hankui Liu

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134033J (2024) https://doi.org/10.1117/12.3051355
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

In order to reduce the economic losses caused by pest infestation during mulberry leaf cultivation, this paper proposes a YOLOv8 detection model integrating lightweight and multi-scale, named YOLOv8s-SLS. The channel-to-feature-to-space channel (C2FSC) module is first introduced in the backbone network to compensate for feature information lost due to model deepening by using complementary information between neighboring regions. Then, the neck structure and the detector head (NLN) were redesigned to improve the recognition of target pests at multiple scales while removing redundant connections in the model. Finally, the LSKA module enhanced the feature representation of the model by dynamically adapting to the sensory field. In addition, a mulberry leaf pest dataset containing different target sizes, named MPD1, consisting of 1705 raw images of three pests, was constructed for model training and validation. The experimental results on the test dataset showed that the parameters of the enhanced and multi-scale versions of the model were reduced by about 15% and the mAP50 was improved by 3.7% compared with the original YOLOv8 model. The experiments proved that the model can quickly and accurately identify pests in mulberry gardens, providing feasible technical support for real-time detection of pests in the sericulture industry.

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

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Zhonghang Liu and Hankui Liu "YOLOv8s-SLS: a mulberry leaves pest detection model integrating lightweight and multiscale", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134033J (18 November 2024); https://doi.org/10.1117/12.3051355

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KEYWORDS

Performance modeling

Target detection

Data modeling

Neck

Head

Ablation

Education and training

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