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

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.