Rapid detection of surface defects based on multi-scale compression CNN

被引：0

作者：

Lian J. ^{[1
]}

He J. ^{[1
]}

Niu Y. ^{[1
]}

Wang T. ^{[1
]}

机构：

[1] School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 11期

关键词：

convolution neural network; multi-scale; network compression; surface defects detection;

D O I：

10.13196/j.cims.2022.11.024

中图分类号：

学科分类号：

摘要：

The application of image processing technology based on various convolution neural network algorithms to detect and identify surface defects can not only reduce the cost of labor, but also greatly improve the efficiency and accuracy. However, the current popular image processing technology has the characteristics of large computation, high storage cost and very complex, which is contrary to the high real-time and limited computing resources required by industrial applications. Therefore a Multi-scale Compression Convolution Neural Network model (MC-CNN) was proposed for the rapid detection of steel surface defects. The multi-scale compression of the network was carried out by network structure optimization, knowledge distillation, network pruning and parameter quantization. The experimental results showed that the proposed method could greatly improve the recognition efficiency, reduce the volume of the model, which was facilitate the application in various scenarios with high real-time requirements and limited storage and computing resources. © 2022 CIMS. All rights reserved.

引用

页码：3624 / 3631

页数：7

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