Detection of Surface Defects of Barrel Media Based on PaE-VGG Model

Hongli Peng, Long Cheng and Jianyan Tian ()
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Hongli Peng: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Long Cheng: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
Jianyan Tian: College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Mathematics, 2025, vol. 13, issue 7, 1-13

Abstract: To address the issues of insufficient defect samples and low detection accuracy of barrel media, we propose a detection of the surface defects of barrel media based on a PaE-VGG model. The proposed PaE-VGG model is based on a modification of a state-of-the-art VGG convolutional neural network, incorporating position-aware circular convolution for facilitating location-sensitive global feature extraction. For each feature extraction channel, the Efficient Channel Attention mechanism is calculated, which adaptively weights the feature vector. The experimental findings demonstrate that our proposed PaE-VGG model achieves an accuracy rate of 94.37%, showcasing a significant improvement of 4.76% compared to the previous version. Furthermore, when compared to highly successful convolutional neural networks for defect detection, such as AlexNet, Googlenet, and ResNet18, our optimization model outperforms them by 4.20%, 1.51%, and 0.72%, respectively. Therefore, the proposed PaE-VGG has achieved good precision and performance in the detection of barrel media defects after improvement.

Keywords: barrel media; defect detection; VGG network; attention mechanism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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