| 摘 要: 针对钢卷尺生产过程中表面缺陷检测效率低下的问题，构建一套应用于实际工业环境下的基于机器视觉的钢卷尺表面缺陷在线检测系统。首先，设计一种实验检测平台用于获取钢卷尺表面的图像；然后，通过图像分割的数字图像处理手段准确定位钢卷尺区域轮廓；最后，采用基于灰度值的模板匹配算法、边缘检测算法及颜色聚类方法对预处理后的图像进行匹配和特征计算，实现对目标物体和区域图像的快速定位和特征提取。结果表明：该检测系统的正确率达95.83%，平均检测速度达5.025 秒/根，基本代替了人工检测，为钢卷尺表面检测提供了一种检查正确率和效率较高的新方法。 |
| 关键词: 机器视觉；钢卷尺；缺陷检测；图像处理 |
|
中图分类号: TP23
文献标识码: A
|
|
| Design of Surface Defect Detection System of Steel Tape based on Machine Vision |
|
CHEN Jiaxing, SHEN Yi, ZHOU Hao, DENG Xiaochen
|
(School of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China )
731224037@qq.com; syzsy@163.com; 1332303265@qq.com; 578045370@qq.com
|
| Abstract: A large gap between the preset degradation model of most face hyper-resolution algorithms and the degradation of real images leads to the poor effect of face reconstruction. In view of this problem, this paper proposes a face super-resolution reconstruction algorithm for real image degradation. Firstly, a hybrid degradation model is designed, which combines motion blur, Gaussian noise and other degradation forms to simulate the real image degradation space and generate low-resolution images close to the real scene. Then, the wavelet coefficients of the high-resolution image are predicted by the super-resolution reconstruction network based on wavelet domain, and the super-resolution image is obtained by inverse wavelet transform. Experimental results on FFHQ (Flickr-Faces-HQ) and RealSR datasets show that the proposed algorithm not only effectively improves the reconstruction effect, but also is suitable for face super-resolution reconstruction in real scenes. |
| Keywords: machine vision; steel tape; defect detection; image processing |
