Defect detection algorithm for cigarette outer packaging based on deep learning

Yizhen Lin; Yong Wang; Jinhua Song; Meng Shu; Haitao Chen; Zhihao Xu

doi:10.1117/12.3012118

21 December 2023 Defect detection algorithm for cigarette outer packaging based on deep learning

Yizhen Lin, Yong Wang, Jinhua Song, Meng Shu, Haitao Chen, Zhihao Xu

Proceedings Volume 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023); 1297038 (2023) https://doi.org/10.1117/12.3012118
Event: Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 2023, Guilin, China

Abstract

In the cigarette production process, detecting defects in the outer packaging is a crucial step. To address the issue of high false recognition rates in factory environments, we propose an improved YOLOv7-tiny target detection algorithm. Our algorithm is based on the YOLOv7-tiny network structure and enhances feature extraction and location by adding the SA module at the end of the Backbone. Additionally, we integrate the GSConv module into the network Neck part to improve information flow and feature fusion ability. Our experimental results show that adding the SA module and the GSConv module to the network increases average accuracy by 2.2% and 3.5%, respectively. After fusing the two, the average detection accuracy is 94.6%. Compared to the original YOLOv7-tiny, our improvements result in a 5.4%increase in accuracy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yizhen Lin, Yong Wang, Jinhua Song, Meng Shu, Haitao Chen, and Zhihao Xu "Defect detection algorithm for cigarette outer packaging based on deep learning", Proc. SPIE 12970, Fourth International Conference on Signal Processing and Computer Science (SPCS 2023), 1297038 (21 December 2023); https://doi.org/10.1117/12.3012118

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KEYWORDS

Detection and tracking algorithms

Defect detection

Packaging

Object detection

Deep learning

Feature extraction

Neck

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