Journal
MEASUREMENT
Volume 186, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2021.110217
Keywords
Roughness; Milled surface; Convolutional neural network; Classification
Funding
- National Natural Science Foundation of China (NSFC) [52065016]
- Guangxi Graduate Student Innovation Project in 2021 [YCSW2021204]
- Guangxi Science and Technology Plan Project [2018GXNSFAA050006]
Ask authors/readers for more resources
This study classifies surface roughness based on deep convolutional neural network method, achieving roughness detection without index design and good light source robustness. The Xception model shows high roughness classification accuracy and robustness in different light source environments, making online measurement of visual roughness possible.
At present, machine vision roughness detection mostly needs to design roughness related indexes based on images, and the index design has human intervention and is heavily dependent on the light source environment. To solve this problem, the paper classifies the surface roughness based on the deep convolutional neural network method, which can realize the roughness detection without index design. The most important thing is that the detection method has good light source robustness under different light source environments. The study adopts an end-to-end image analysis method, by means of image enhancement pre-processing of a small number of source images, after multi-layer convolution and pooling operations, as well as comprehensive processing of fully connected and classification layers, the convolutional kernel can automatically extract the features of the image, and finally the surface roughness obtained by vertical disc cutter milling can be classified and predicted. In addition, based on the experimental light source environment with two different luminance of night and day, by comparing with the technically mature ResNet50 and DenseNet121 convolutional neural network models, the deep convolutional neural network Xception model not only has high roughness classification accuracy, but also has more light source environment robustness. This method makes the online measurement of visual roughness possible.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available