Journal's Facebook pageA Lightweight defect classification Method for Latex Gloves Based on Image Enhancement
- Applied Technology College Of Soochow University
215325 SuZhou,China
renyong@suda.edu.cn - Suzhou Dechuang Measurement and Control Technology Co., Ltd.
215104 Suzhou, China
Abstract
This paper presents a glove defect classification method that integrates image enhancement techniques with a lightweight model to enhance the efficiency and accuracy of glove defect classification in industrial manufacturing. A dataset comprising images of five types of gloves was collected, totaling 360 sample images, for the training and validation of a deep learning-based glove defect classification model. Image enhancement techniques, including super-pixels, exposure adjustment, blurring, and limited contrast adaptive histogram equalization, increased dataset diversity and size, improving model generalization. Based on the lightweight model MobileNetV2, the model was improved by reducing the number of input image channels through grayscale conversion and optimizing the loss function. Experimental results demonstrate that the improved MobileNetV2 model achieved an average accuracy of 97.85% on both the original and enhanced datasets, effectively mitigated overfitting phenomena, and exhibited a significantly faster training speed compared to the ResNet34 and ResNet50 models.
Key words
glove defect classification, machine vision, image enhancement, deep learning, lightweight model, mobilenetv2
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS240911007R
Publication information
Volume 22, Issue 1 (January 2025)
Year of Publication: 2025
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
Available in PDF
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How to cite
Ren, Y., Liu, D., Gu, S.: A Lightweight defect classification Method for Latex Gloves Based on Image Enhancement. Computer Science and Information Systems, Vol. 22, No. 1, 181–197. (2025), https://doi.org/10.2298/CSIS240911007R
