One-stage defect detection method for plastic caps

Xuebin Hong; Weiwei Zhao; Jubin Huang; Huiwen Zou; Yuecheng Chen

doi:10.1117/12.3011997

7 December 2023 One-stage defect detection method for plastic caps

Xuebin Hong, Weiwei Zhao, Jubin Huang, Huiwen Zou, Yuecheng Chen

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 1294150 (2023) https://doi.org/10.1117/12.3011997
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

This paper presents a plastic cap defect detection model. Plastic caps play a crucial role in industrial production, but they are susceptible to various defects caused by factors such as raw materials and manufacturing processes. Traditional defect detection methods rely on complex feature engineering and classifiers, leading to limited accuracy. To overcome these limitations, this study proposes a defect detection solution that leverages the YOLO model's renowned fast and end-to-end detection capability. By training on a substantial datasets of labelled plastic cap images, an efficient and accurate defect detection model is constructed. Specifically optimized for plastic cap defects, the model achieves a remarkable accuracy of 96% with low false positive and false negative rates. Comparative experiments and evaluations validate the superior efficiency and accuracy of the proposed method compared to traditional approaches. Consequently, this study presents a highly effective solution for plastic cap defect detection.

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

Citation Download Citation

Xuebin Hong, Weiwei Zhao, Jubin Huang, Huiwen Zou, and Yuecheng Chen "One-stage defect detection method for plastic caps", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 1294150 (7 December 2023); https://doi.org/10.1117/12.3011997

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KEYWORDS

Defect detection

Plastics

Object detection

Deep learning

Education and training

Detection and tracking algorithms

Feature extraction

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