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A Benchmark for Automated Vickers Hardness Testing
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AI
DOI：10.1007/978-3-031-97822-7_1.png)
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Abstract
En 中文
Automated Vickers hardness measurement methods must be robust and versatile to cope with materials with rough and noisy patterned surfaces. The behaviour of several state-of-the-art algorithms for automated Vickers hardness measurement is evaluated and compared for their respective robustness and accuracy. The evaluation is based on real world industrial data, collected in a Vickers indentation dataset. Addressing the current lack of publicly available (annotated) Vickers indentation data, and in order to provide a benchmark for further investigations and experiments, we release the databases used in this work, together with expert groundtruth covering diagonal measurement and segmentation masks, and make them available on-line for public open access.
Keywords:
Vickers Hardness Testing
Deep-learning
Automated Vickers Hardness Measurement
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