Survey of Scratch Detection Technology Based on Machine Vision

Fig. 1. Simulation experimental results of multi-frame iterative deconvolution algorithm^[22]. (a) Original image; (b) segmentation result $T_{0}$ ; (c) segmentation result $T_{45}$ ; (d) segmentation result $T_{90}$ ; (e) segmentation result $T_{135}$ ; (f) final result $T$

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Fig. 2. Operator templates in 45°, 135°, 180°, 225°, 270°, 315°, horizontal, and vertical directions^[23]

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Fig. 3. Schematic of two-level labeling technique^[26]

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Fig. 4. Optimized elliptical Gabor filter and its adjustments^[30]. (a) (b) Optimized elliptical Gabor filter; (c) (d) adjusting to ring Gabor filter; (e) (f) adjusting to ring Gabor filter

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Fig. 5. Example of a typical CNN architecture

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Table 1. Advantages and disadvantages of different scratch detection methods based on manual design feature

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Table 1. Advantages and disadvantages of different scratch detection methods based on manual design feature

Taxonomy	Method	Advantage	Disadvantage
Manual design feature	Gray distribution statistics	Reflect the regularity and local features of the image	Sensitive to noise，suitable for low resolution images
	Transform domain methods	Strong anti-interference ability to noise and changing illumination	Lack of local information，susceptible to feature correlation
	High- and low-dimensional space mapping methods	Strong anti-interference ability to noise，strong adaptability	Poor detection for low contrast or fine scratches

Table 2. Advantages and disadvantages of different deep learning scratch detection methods

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Table 2. Advantages and disadvantages of different deep learning scratch detection methods

Taxonomy	Method	Advantage	Disadvantage
Deep learning	Supervised learning	Has high detection accuracy，less susceptible to light and noise	Requires a large number of labeled images as training data sets
Deep learning	Unsupervised learning	Does not require tagging data sets and human intervention	Susceptible to light，noise and initial values of network parameters

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Lemiao Yang, Fuqiang Zhou. Survey of Scratch Detection Technology Based on Machine Vision[J]. Laser & Optoelectronics Progress, 2022, 59(14): 1415009

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