A Detection and Identification Method Based on Machine Vision for Bearing Surface Defects

Surface defects on bearings can directly affect the service life and reduce the performance of equipment. At present, the detection of bearing surface defects is mostly done manually, which is labor-intensive and results in poor stability. To improve the inspection speed and the defect recognition rate, we proposed a bearing surface defect detection and classification method using machine vision technology. The method makes two main contributions. It proposes a local multi-neural network (Lc-MNN) image segmentation algorithm with the wavelet transform as the classification feature. The precision segmentation of the defect image is accomplished in three steps: wavelet feature extraction, Lc-MNN region division, and Lc-MNN classification. It also proposes a feature selection algorithm (SCV) that makes comprehensive use of scalar feature selection, correlation analysis, and vector feature selection to first remove similar features through correlation analysis, further screen the results with a scalar feature selection algorithm, and finally select the classification features using a feature vector selection algorithm. Using 600 test samples with three types of defect in the experiment, an identification rate of 99.5% was achieved without the need for large-scale calculation. The comparison tests indicated that the proposed method can achieve efficient feature selection and defect classification.

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Segmented Embedded Rapid Defect Detection Method for Bearing Surface Defects

Machines ◽

10.3390/machines9020040 ◽

2021 ◽

Vol 9 (2) ◽

pp. 40

Author(s):

Linjian Lei ◽

Shengli Sun ◽

Yue Zhang ◽

Huikai Liu ◽

Hui Xie

Keyword(s):

Machine Vision ◽

Defect Detection ◽

Character Recognition ◽

Surface Defect ◽

Surface Defects ◽

Detection Method ◽

Rapid Development ◽

Detection Accuracy ◽

Bearing Surface ◽

Surface Defect Detection

The rapid development of machine vision has prompted the continuous emergence of new detection systems and algorithms in surface defect detection. However, most of the existing methods establish their systems with few comparisons and verifications, and the methods described still have various problems. Thus, an original defect detection method: Segmented Embedded Rapid Defect Detection Method for Surface Defects (SERDD) is proposed in this paper. This method realizes the two-way fusion of image processing and defect detection, which can efficiently and accurately detect surface defects such as depression, scratches, notches, oil, shallow characters, abnormal dimensions, etc. Besides, the character recognition method based on Spatial Pyramid Character Proportion Matching (SPCPM) is used to identify the engraved characters on the bearing dust cover. Moreover, the problem of characters being cut in coordinate transformation is solved through Image Self-Stitching-and-Cropping (ISSC). This paper adopts adequate real image data to verify and compare the methods and proves the effectiveness and advancement through detection accuracy, missing alarm rate, and false alarm rate. This method can provide machine vision technical support for bearing surface defect detection in its real sense.

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Detection of Fruit Surface Defects Based on Machine Vision

Journal of Physics Conference Series ◽

10.1088/1742-6596/1952/2/022048 ◽

2021 ◽

Vol 1952 (2) ◽

pp. 022048

Author(s):

Yayue Cao

Keyword(s):

Machine Vision ◽

Surface Defects ◽

Fruit Surface

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A method for classifying citrus surface defects based on machine vision

Journal of Food Measurement & Characterization ◽

10.1007/s11694-021-00868-z ◽

2021 ◽

Author(s):

Wenzhuo Zhang ◽

Aijiao Tan ◽

Guoxiong Zhou ◽

Aibin Chen ◽

Mingxuan Li ◽

...

Keyword(s):

Machine Vision ◽

Surface Defects

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Ultra-precision detection of surface defects of large aperture diffraction grating based on machine vision

10.1117/12.2606743 ◽

2021 ◽

Author(s):

Haotian Wang ◽

Chaoming Li ◽

Xinrong Chen ◽

Zhe Huang ◽

Jiayao Pan ◽

...

Keyword(s):

Machine Vision ◽

Diffraction Grating ◽

Surface Defects ◽

Large Aperture ◽

Ultra Precision ◽

Aperture Diffraction

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POSE ROBUST HUMAN DETECTION IN DEPTH IMAGES USING MULTIPLY-ORIENTED 2D ELLIPTICAL FILTERS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001410008135 ◽

2010 ◽

Vol 24 (05) ◽

pp. 691-717 ◽

Cited By ~ 2

Author(s):

SANG-HO CHO ◽

TAEWAN KIM ◽

DAIJIN KIM

Keyword(s):

Human Identification ◽

Human Detection ◽

Stereo Images ◽

Bhattacharyya Distance ◽

Identification Method ◽

Depth Images ◽

Detection And Identification ◽

The Face ◽

Human Pose ◽

Spatial Depth

This paper proposes a pose robust human detection and identification method for sequences of stereo images using multiply-oriented 2D elliptical filters (MO2DEFs), which can detect and identify humans regardless of scale and pose. Four 2D elliptical filters with specific orientations are applied to a 2D spatial-depth histogram, and threshold values are used to detect humans. The human pose is then determined by finding the filter whose convolution result was maximal. Candidates are verified by either detecting the face or matching head-shoulder shapes. Human identification employs the human detection method for a sequence of input stereo images and identifies them as a registered human or a new human using the Bhattacharyya distance of the color histogram. Experimental results show that (1) the accuracy of pose angle estimation is about 88%, (2) human detection using the proposed method outperforms that of using the existing Object Oriented Scale Adaptive Filter (OOSAF) by 15–20%, especially in the case of posed humans, and (3) the human identification method has a nearly perfect accuracy.

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