Surface Defects Detection of Stamping and Grinding Flat Parts Based on Machine Vision

Tian, Hongzhi; Wang, Dongxing; Lin, Cheng-An J.; Chen, Qilin; Liu, Zhaocai

doi:10.3390/s20164531

Cited by 18 publications

(11 citation statements)

References 27 publications

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“…Therefore, under a fixed viewing angle, the high-reflection areas, Ar1 and Ar2, can only be generated by light source LS2 in the incident optical path, IL–RL; however, this is not true for other light sources, such as LS3. Therefore, it is possible to obtain image sequences with highly reflective position differences by separately illuminating and imaging with small light sources at different positions to recover surface information through image fusion [ 26 , 28 ].…”

Section: Methodsmentioning

confidence: 99%

Method and Device of All-in-Focus Imaging with Overexposure Suppression in an Irregular Pipe

Wang

Xing

et al. 2022

Sensors

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To avoid depth-of-field mismatches caused by the changes in pipe structure and image overexposures caused by highly reflective surfaces while radial imaging irregular pipes, this paper proposes a novel all-in-focus, adaptable, and low scene-coupling method that suppresses overexposures in support of fault detection. Firstly, the pipeline’s radial depth distribution data are obtained by sensors, and an optimal all-in-focus imaging scheme is established by combining camera parameters. Secondly, using digital imaging technology, the high reflection effect produced by disparate light sources is comprehensively evaluated for overexposure suppression. Thirdly, a device is designed for imaging non-Lambertian free-form surface scenes under low illumination, providing the sequence images needed for the next step. Lastly, specific digital fusions are made to the sequential images to obtain an all-in-focus final image without overexposure. An image-quality analysis method is then used to measure the efficacy of the system in obtaining the characteristic information of the inner surfaces of an irregular pipe. Results of the experiment show that the method and device used are able to distinguish small 0.5 mm wide lines ranging from 40–878 mm depth and are capable of providing efficient image support for defect inspection of irregular pipes and free-form surfaces amongst other irregular surfaces.

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Section: Methodsmentioning

confidence: 99%

Method and Device of All-in-Focus Imaging with Overexposure Suppression in an Irregular Pipe

Wang

Xing

et al. 2022

Sensors

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“…A multi-scale defect recognition method was proposed by Yu Jiahui et al which has good accuracy and detection speed [10]. Tian Hongzhi et al designed a micro defect detection system for grinding surface by combining plane illumination mode with multi angle illumination mode [11]. Mentouri zoheir et al employed an improved dual cross algorithm to online monitoring of steel surface quality [12].…”

Section: Related Workmentioning

confidence: 99%

Cylinder Liner Defect Detection and Classification based on Deep Learning

Gao¹,

Fei²,

Song³

et al. 2022

IJACSA

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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.

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“…18 In the industrial field, for segmenting small objects in a reflective background, many researchers have made efforts. Tian et al 19 propose a multi-angle light system. They reduce the influence of reflective background by multi-angle illumination and adjusting the exposure time of camera.…”

Section: Related Workmentioning

confidence: 99%

Visual detection of residual cut tobacco based on multi-feature fusion and Markov random field

Liu

Zhang

et al. 2022

J. Electron. Imag.

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Image segmentation has been commonly used for segmenting regions of interest in the industrial field. However, segmenting small objects in a reflective background is a complex task that limits the performance of image segmentation in the industry. We take residual cut tobacco in industrial production as an object and propose an automatic detection method. This method overcomes the influence of reflective background and achieves accurate detection of small-area residual cut tobacco. The proposed method combines multi-feature fusion method and Markov random field model to construct a coarse-to-fine segmentation framework. The coarse part is used to eliminate the effect of specular highlights and specular reflection. The fine part is used to detect and clear small regions of residual cut tobacco that are wrongly segmented by the coarse part. We collect 2411 images in the real industrial production line for evaluation. The experimental results show that the proposed method yields an averaged precision and mean intersection over union of 95.85% and 86.71%, which outperforms some detection methods based on deep learning.

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Surface Defects Detection of Stamping and Grinding Flat Parts Based on Machine Vision

Cited by 18 publications

References 27 publications

Method and Device of All-in-Focus Imaging with Overexposure Suppression in an Irregular Pipe

Method and Device of All-in-Focus Imaging with Overexposure Suppression in an Irregular Pipe

Cylinder Liner Defect Detection and Classification based on Deep Learning

Visual detection of residual cut tobacco based on multi-feature fusion and Markov random field

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