Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin 300387, China.
College of Mechanical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China.
Sensors (Basel). 2018 Oct 31;18(11):3709. doi: 10.3390/s18113709.
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.
本文提出了一种基于深度卷积神经网络（CNN）的技术，用于检测金属螺丝表面的微缺陷。我们考虑的缺陷包括表面损伤、表面污垢和螺丝滑牙。使用工业相机采集不同类型缺陷的金属螺丝图像，然后利用这些图像来训练设计好的深度 CNN。为了实现高效检测，我们首先在相机拍摄的图片中定位螺丝表面，从而提取出螺丝表面的图像，然后将这些图像输入到基于 CNN 的缺陷检测中。实验结果表明，该技术的检测准确率可达 98%；每张图片的平均检测时间为 1.2 秒。与传统机器视觉技术（例如基于模板匹配的技术）的比较表明，所提出的基于深度 CNN 的技术具有优越性。
