Express packaging defects detection model based on YOLOv8

Ziye Zhang; Xiangquan Chang

doi:10.1117/12.3038509

23 August 2024 Express packaging defects detection model based on YOLOv8

Ziye Zhang, Xiangquan Chang

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 1325032 (2024) https://doi.org/10.1117/12.3038509
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

This paper proposes the SFN-YOLOv8 detection model for the detection of express packaging defects. This model has the following characteristics: it uses lightweight ShuffleNetV2 as the backbone network of the model and adopts deformable convolution (DCN), thus reducing the number of model parameters. The C2F-CPCA module is proposed in the neck part to enhance the expressive ability of the network in feature representation. Wiou is used in the loss function to reduce the impact of large gradients or harmful gradients appearing in extreme samples in the data set on the loss calculation. Experimental results indicate that compared with the initial model, the SFN-YOLOv8 model has reduced its parameters by 0.15M, decreased GFLOPs by 0.8G, and improved mAP@0.5 by 1.9%.

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

Citation Download Citation

Ziye Zhang and Xiangquan Chang "Express packaging defects detection model based on YOLOv8", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 1325032 (23 August 2024); https://doi.org/10.1117/12.3038509

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KEYWORDS

Packaging

Defect detection

Data modeling

Convolution

Performance modeling

Object detection

Target detection

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