Micro-Scale Surface Recognition via Microscope System Based on Hu Moments Pattern and Micro Laser Line Projection

Rodríguez, José Mendoza

doi:10.3390/met13050889

Cited by 1 publication

(1 citation statement)

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“…This technique utilizes microlasers to project very fine laser lines, and obtains information about the fine features of the surface by observing the reflection or scattering of the laser lines on the surface to be recognized, so as to infer the structure, texture or defects of the surface. Rodríguez 38 presented the recognition of micrometer scale concave, convex. and planar surfaces by means of Hu moment patterns based on micro-laser line profiles.…”

Section: Defect Detection Modelsmentioning

confidence: 99%

STD-Detector: spatial-to-depth feature-enhanced detection method for the surface defect detection of strip steel

Wang,

Liu,

Yang

et al. 2023

J. Electron. Imag.

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Due to the small size, high density, and background noise associated with strip surface defects, the current object detection model commonly faces limitations in performance. To address this issue, we propose a spatial-to-depth feature-enhanced detection method called STD-Detector. The method consists of two types STD-Conv-A and STD-Conv-B. First, the STD-Conv-A module is used in the backbone feature extraction network to expand the field of perception and enable the model to learn a wider range of background information. Then, the STD-Conv-B module is used for feature fusion networks to improve the expression of output features. In addition, we incorporate the convolutional block attention module to mitigate background interference and enhance the performance of the model. Finally, experimental results on the NEU-DET dataset show that our method achieves a mean average precision of 82.9%, which represents a 3.9% improvement over the baseline. Compared with the state-of-the-art model, our method exhibits greater competitiveness in detecting strip surface defects. Moreover, experimental results on the road surface defect dataset show that our method has good robustness.

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Section: Defect Detection Modelsmentioning

confidence: 99%