Journal
SENSORS
Volume 18, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/s18113709
Keywords
metal screw surface; deep convolutional neural network; micro-defect detection
Funding
- National Natural Science Foundation of China [61078041, 51806150]
- Natural Science Foundation of Tianjin [16JCYBJC15400, 15JCYBJC51700, 18JCQNJC04400]
- National Natural Science Foundation Committee
- Tianjin Research Program of Application Foundation and Advanced Technology
- State Key Laboratory of Precision Measuring Technology and Instruments (Tianjin University) [PIL1603]
- Program for Innovative Research Team in University of Tianjin [TD13-5036]
- Tianjin Enterprise Science and Technology Commissioner Project [18JCTPJC61700]
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This paper proposes a deep convolutional neural network (CNN) -based technique for the detection of micro defects on metal screw surfaces. The defects we consider include surface damage, surface dirt, and stripped screws. Images of metal screws with different types of defects are collected using industrial cameras, which are then employed to train the designed deep CNN. To enable efficient detection, we first locate screw surfaces in the pictures captured by the cameras, so that the images of screw surfaces can be extracted, which are then input to the CNN-based defect detector. Experiment results show that the proposed technique can achieve a detection accuracy of 98%; the average detection time per picture is 1.2 s. Comparisons with traditional machine vision techniques, e.g., template matching-based techniques, demonstrate the superiority of the proposed deep CNN-based one.
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