Research on surface defect detection method of steel parts based on barkhausen effect

2021 ◽  
Author(s):  
Huayu Zhang ◽  
Zhichen Gong ◽  
Fengqin Xie ◽  
Haixia Wang
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Detection Method ◽  
Barkhausen Effect ◽  
Surface Defect Detection

scholarly journals Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

2018 ◽  
Vol 8 (9) ◽  
pp. 1678 ◽  
Author(s):  
Yiting Li ◽  
Haisong Huang ◽  
Qingsheng Xie ◽  
Liguo Yao ◽  
Qipeng Chen
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Method ◽  
Detection Algorithm ◽  
Single Shot ◽  
Detection Model ◽  
Machine Learning Methods ◽  
Network Methods ◽  
Surface Defect Detection

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.

A surface defect detection method based on multi-feature fusion

2017 ◽  
Author(s):  
Xiaojun Wu ◽  
Huijiang Xiong ◽  
Zhiyang Yu ◽  
Peizhi Wen
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Detection Method ◽  
Feature Fusion ◽  
Surface Defect Detection

Surface Defect Detection Method of Ceramic Bowl Based on Kirsch and Canny Operator

2016 ◽  
Vol 36 (9) ◽  
pp. 0904001 ◽  
Author(s):  
郭萌 Guo Meng ◽  
胡辽林 Hu Liaolin ◽  
赵江涛 Zhao Jiangtao
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Detection Method ◽  
Canny Operator ◽  
Surface Defect Detection
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