Abstract: 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.
Song et al. REF considered the defect detection problem of surface damage, surface dirt, and stripped screws; proposed the screw surface defect detection technology based on the CNN; and proved that deep learning technology was better than the traditional template matching technology.
Detection of Micro-Defects on Metal Screw Surfaces Based on Deep Convolutional Neural Networks
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Engineering Computer Science Medicine