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Surface crack detection using deep learning with shallow CNN architecture for enhanced computation

Research article (Neural Computing and Applications, 2021) · cited 209× · AI/ML
academia openalex:W3125559191
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Surface crack detection using deep learning with shallow CNN architecture for enhanced computation

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Surface crack detection using deep learning with shallow CNN architecture for enhanced computation is a scholarly article[1].

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  • Surface crack detection using deep learning with shallow CNN architecture for enhanced computation's instance of is recorded as scholarly article[2].

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  1. [2] ↑ . wikidata.org.

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Surface crack detection using deep learning with shallow CNN architecture for enhanced computation. Retrieved May 24, 2026, from https://4ort.xyz/entity/surface-crack-detection-using-deep-learning-with-shallow-cnn-architecture-for-enhanced-computation
MLA “Surface crack detection using deep learning with shallow CNN architecture for enhanced computation.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/surface-crack-detection-using-deep-learning-with-shallow-cnn-architecture-for-enhanced-computation.
BibTeX @misc{4ortxyz_surface-crack-detection-using-deep-learning-with-shallow-cnn-architecture-for-enhanced-computation_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Surface crack detection using deep learning with shallow CNN architecture for enhanced computation}}, year = {2026}, url = {https://4ort.xyz/entity/surface-crack-detection-using-deep-learning-with-shallow-cnn-architecture-for-enhanced-computation}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Surface crack detection using deep learning with shallow CNN architecture for enhanced computation — https://4ort.xyz/entity/surface-crack-detection-using-deep-learning-with-shallow-cnn-architecture-for-enhanced-computation (retrieved 2026-05-24)

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