Author(s)

Jiafeng Li ¹, Chenxi Yan ²

Affiliation(s)

¹ College of Software Engineering, Sichuan University, Chengdu, 610207, China
² School of Computer Science, Northeast Electric Power University, Jilin, Jilin, 132011, China

Corresponding Author

ABSTRACT

YOLOv8s model often used at Object detection which is widely used in many fields, cannot aggregate feature information well for a specific task, and the number of parameters is large and the accuracy is not high. Aiming at the above problems of traditional YOLOv8s, a new lightweight and performance-balanced YOLOv8s network structure is proposed. The C2F module in the backbone network are replaced with ShuffleNet-v2, and in order to further improve the accuracy degradation due to the decrease in the number of parameters, a global multi-attention mechanism is added to obtain global information, learn the correlation between features at different scales, and fuse them. SGD is used as an optimizer to further improve accuracy. The experimental results show that the introduction of ShuffleNet-v2 and MHSA effectively reduces the number of parameters of the model, significantly reduces the training time, and the accuracy is considerable, and compared with other optimizers SGD has the largest performance improvement, and has an excellent performance in terms of the balance between lightweighting and algorithmic performance.

KEYWORDS

CITE THIS PAPER

Jiafeng Li, Chenxi Yan, YOLOv8 with Multi Strategy Integrated Optimization and Application in Object Detection. Automation and Machine Learning (2024) Vol. 5: 23-31. DOI: http://dx.doi.org/10.23977/autml.2024.050104.

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