Detection and Classification of Metal Workpiece Surface Defects Based on Machine Vision

2021 ◽  
Author(s):  
Yibei Huang ◽  
Tao Yu ◽  
Kai Wan ◽  
Jie Yuan
Keyword(s):  
Machine Vision ◽  
Surface Defects ◽  
Workpiece Surface

Surface Defect Inspection and Classification of Segment Magnet by Using Machine Vision Technique

2011 ◽  
Vol 339 ◽  
pp. 32-35 ◽  
Author(s):  
Hong Hai Jiang ◽  
Guo Fu Yin
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Surface Defects ◽  
Processing System ◽  
Support Vector ◽  
Inspection System ◽  
Low Contrast ◽  
Constituent Material ◽  
Canny Edge

In this paper, we propose a machine vision based approach for detecting and classifying irregular low-contrast surface defects of segment magnet. The constituent material of it is ferrite which varies from silver gray to black in color .For this reason, the defects embedded in a low-contrast surface show no big different from its surrounding region, and even worse, all the surfaces and chamfers of segment magnet must be inspected. Our system is able to analyze all surfaces under inspection, to discover and classify its defects by means of image processing algorithms and support vector machine (SVM). A working prototype of the system has been built and tested to validate the proposed approach and to reproduce the difficult issues of the inspection system. The developed prototype includes three subsystems: an array of several CCD area cameras (Fig.1); a controllable roller LED light source(Fig.1); and a PC-based image processing system. The detection of the defects is performed by means of Canny edge detection, morphology and other feature extraction operations. The image processing and classification results demonstrate that the proposed method can identify surface defects effectively.

Recognition and classification of surface defects of aluminum castings based on machine vision

2021 ◽  
Author(s):  
Jing Lin ◽  
Ke Wen ◽  
Yongyue Liu ◽  
Xuechang Zhang
Keyword(s):  
Machine Vision ◽  
Surface Defects ◽  
Aluminum Castings

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

A method for classifying citrus surface defects based on machine vision

2021 ◽  
Author(s):  
Wenzhuo Zhang ◽  
Aijiao Tan ◽  
Guoxiong Zhou ◽  
Aibin Chen ◽  
Mingxuan Li ◽  
...  
Keyword(s):  
Machine Vision ◽  
Surface Defects
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