Abstract
Surface defect inspection is vital for the quality control of products and the fault diagnosis of equipment. Defect inspection remains challenging due to the low level of automation in some manufacturing plants and the difficulty in identifying defects. To improve the automation and intelligence levels of defect inspection, a CNN model is proposed for the high-precision defect inspection of USB components in the actual demands of factories. First, the defect inspection system was built, and a dataset named USB-SG, which contained five types of defects—dents, scratches, spots, stains, and normal—was established. The pixel-level defect ground-truth annotations were manually marked. This paper puts forward a CNN model for solving the problem of defect inspection tasks, and three strategies are proposed to improve the model’s performance. The proposed model is built based on the lightweight SqueezeNet network, and a rich feature extraction block is designed to capture semantic and detailed information. Residual-based progressive feature integration is proposed to fuse the extracted features, which can reduce the difficulty of model fine-tuning and improve the generalization ability. Finally, a multi-step deep supervision scheme is proposed to supervise the feature integration process. The experiments on the USB-SG dataset prove that the model proposed in this paper has better performance than that of other methods, and the running speed can meet the real-time demand, which has broad application prospects in the industrial inspection scene.
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering
Reference53 articles.
1. Vision-based automatic detection of steel surface defects in the cold rolling process: Considering the influence of industrial liquids and surface textures;Zhao;Int. J. Adv. Manuf. Technol.,2017
2. Yang, L., Huang, X., Ren, Y., and Huang, Y. (2022). Steel Plate Surface Defect Detection Based on Dataset Enhancement and Lightweight Convolution Neural Network. Machines, 10.
3. Review of vision-based steel surface inspection systems;Neogi;EURASIP J. Image Video Process.,2014
4. Fabric defect detection using activation layer embedded convolutional neural network;Ouyang;IEEE Access,2019
5. Automated visual inspection of glass bottle bottom with saliency detection and template matching;Zhou;IEEE Trans. Instrum. Meas.,2019
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献