A Novel Compact Convolutional Neural Network for Real-Time Nondestructive Evaluation of Metallic Surfaces

Borwankar, Raunak; Ludwig, Reinhold

doi:10.1109/tim.2020.2990541

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…Due to continuous rapid advance of both hardware and software technologies in camera and computing systems, Vision-Based Measurement had gained much research interest [1], as compared with the human eye, a camera can capture more information and process information in less time. For example, using machine vision to evaluate the metallic surfaces [2,3]. Xuebing et al employed a camera to obtain a workpiece image, which they applied to realize precise measurements [4].…”

Section: Introductionmentioning

confidence: 99%

Machine vision based Vernier caliper reading technology research

Chen,

Wang,

Wang

et al. 2022

Metrology and Measurement Systems

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Accurate and fast access to Vernier caliper readings is a critical issue in automated verification of Vernier calipers. To address this problem, this paper proposes a machine vision-based algorithm for reading the Vernier caliper's displayed value. The suggested method first employs threshold segmentation and template matching to determine the region of interest and obtain the main ruler digit position by alternate projection. Then, we apply the improved LeNet5 network to identify the main ruler of the Vernier caliper, Moreover, we developed the first and last inscription method for reading the decimal part of the Vernier caliper and established our data set for model training. Extensive experiments on reading the displayed value have demonstrated our algorithm's accuracy, which achieves a displayed value reading accuracy of 100%. Compared to other methods, the proposed technique affords better stability and accuracy.

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Section: Introductionmentioning

confidence: 99%