摘要
针对金属工件表面小尺寸缺陷及受非均匀光照影响的图像缺陷难以分割的问题,提出了一种改进的U-net语义分割网络,实现金属工件表面缺陷图像的精确分割。首先,在U-net网络中融入CBAM(convolutional block attention module)模块来提升图像中缺陷目标的显著度;其次,采用深度超参数化卷积DO-Conv(depthwise over-parameterized convolutional)代替网络中部分传统卷积,增加网络可学习的参数数量;然后,采用Leaky Relu函数代替网络中部分Relu函数,提高模型对负区间的特征提取能力;最后,采用中值滤波及非均匀光照的补偿方法进行图像预处理,减弱非均匀光照对金属工件图像表面缺陷的影响。结果表明:改进后的网络平均交并比、准确率和Dice系数指标分别达到0.833 5、0.933 2、0.867 4,改进的网络显著提升了对金属工件表面缺陷图像的分割效果。
For the small-size defects of metal workpiece surface and the difficult segmentation of image defects due to non-uniform illumination, an improved U-net semantic segmentation network was proposed to achieve accurate image segmentation of surface defects for metal workpieces. Firstly, the convolutional block attention module(CBAM) was integrated into the U-net netwok to improve the significance of the defective targets in the image. Secondly, part of the traditional convolution in the network was replaced by depthwise over-parameterized convolution(DO-Conv) to increase the number of learnable parameters of the network. Then,the Leaky Relu function was used instead of the partial Relu function in the network to improve the feature extraction ability of the model for the negative intervals. Finally, the median filtering and non-uniform illumination compensation method were used for image preprocessing, so as to reduce the effect of non-uniform illumination on the surface defects of metal workpiece images. The results show that the improved network mean intersection over union, accuracy rate and Dice coefficient index reaches 0.833 5, 0.933 2 and 0.867 4, respectively. The improved network significantly improves the segmentation effect of surface defect images of metal workpieces.
作者
王一
龚肖杰
苏皓
WANG Yi;GONG Xiaojie;SU Hao(College of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,China;Tangshan Technology Innovation Center of Intellectualization of Metal Component Production Line,Tangshan 063210,China;Tangshan Key Laboratory of Semiconductor Integrated Circuits,Tangshan 063210,China)
出处
《应用光学》
CAS
北大核心
2023年第1期86-92,共7页
Journal of Applied Optics
基金
河北省高等学校科学技术研究项目(ZD2022114)
唐山市科技计划项目(21130212C)。
关键词
表面缺陷
图像分割
语义分割网络
卷积注意力模块
深度超参数化卷积
surface defects
image segmentation
semantic segmentation network
convolutional block attention module
depthwise over-parameterized convolution
