Application of image processing methods for the characterization of selected features and wear analysis in surface topography measurements

Podulka, Przemysław

doi:10.1016/j.promfg.2021.06.018

Cited by 3 publications

(1 citation statement)

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“…Conventional methods for identifying defects on steel surfaces mainly use the wavelet transform [13,14] double-threshold binarization [15,16], and decision trees [17,18] to analyze and detect images. In addition to this, Mukhopadhyay et al used multi-scale morphological segmentation of gray-scale images to process surface image data [19]; Podulka et al used surface topographic image (STI) processing to characterize selected features from the surface texture [20]; and Ravimal et al used the intensity of the near-field contrast image after reflecting light and reflective mirrors, as well as photometric stereoscopic techniques to recover the normal of the surface mapping for automated surface inspection [21]. By adopting these techniques, great strides have been made in optimizing productivity and improving product quality.…”

Section: Classification Of Steel Defectsmentioning

confidence: 99%

Low-Resolution Steel Surface Defects Classification Network Based on Autocorrelation Semantic Enhancement

Guo,

Gong,

2023

Coatings

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Aiming at the problems of low-resolution steel surface defects imaging, such as defect type confusion, feature blurring, and low classification accuracy, this paper proposes an autocorrelation semantic enhancement network (ASENet) for the classification of steel surface defects. It mainly consists of a backbone network and an autocorrelation semantic enhancement module (ASE), in which the autocorrelation semantic enhancement module consists of three main learnable modules: the CS attention module, the autocorrelation computation module, and the contextual feature awareness module. Specifically, we first use the backbone network to extract the basic features of the image and then use the designed CS attention module to enhance the basic features. In addition, to capture different aspects of semantic objects, we use the autocorrelation module to compute the correlation between neighborhoods and contextualize the basic and augmented features to enhance the recognizability of the features. Experimental results show that our method produces significant results, and the classification accuracy reaches 96.24% on the NEU-CLS-64 dataset. Compared with ViT-B/16, Swin_t, ResNet50, Mobilenet_v3_small, Densenet121, Efficientnet_b2, and baseline, the accuracy is 9.43%, 5.15%, 4.87%, 3.34%, 3.28%, 3.01%, and 2.72% higher, respectively.

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