Abstract: The machine vision-based defect detection for cylinder liner is a challenging task due to irregular shape, various and small defects on the cylinder liner surface. To improve the accuracy of defect detection by machine vision a deep learning-based defect detection method for cylinder liner was explored in this paper. First, a machine vision system was designed based on the analysis of the causes and types of defects to obtain the field images for establishing an original dataset. Then the dataset was augmented by a modified augmentation method which combines the region of interest automatic extraction method with the traditional augmentation methods. Except for introduction of the anchor configuration optimization method, an XML file-based method of highlighting defect area was proposed to address the problem of tiny defect detection. The optimal model was experimentally determined by considering the network model, the training strategy and the sample size. Finally, the detection system was developed and the network model was deployed. Experiments are carried out and the results of the proposed method compared with those of the traditional methods. The results show that the detection accuracies of sand, scratch and wear defects are 77.5%, 70% and 66.3% which are improved by at least 26.3% compared with the traditional methods. The proposal can be used for field defect detection of cylinder liner.

Chengchong Gao, Fei Hao, Jiatong Song, Ruwen Chen, Fan Wang and Benxue Liu, “Cylinder Liner Defect Detection and Classification based on Deep Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130818

@article{Gao2022,
title = {Cylinder Liner Defect Detection and Classification based on Deep Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130818},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130818},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Chengchong Gao and Fei Hao and Jiatong Song and Ruwen Chen and Fan Wang and Benxue Liu}
}