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RETRACTED ARTICLE: Capacitance pin defect detection based on deep learning

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This article was retracted on 27 March 2024

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Abstract

Mask R-CNN network based on deep learning algorithm is specifically optimized for the small visual defects of gears. After comparison, the ResNet-101 residual neural network is used as the image sharing feature for network extraction. Subsequently, the unreasonable convolution of the feature extraction process \({P}_{5}\) in the characteristic pyramid network is removed to improve the defect detection rate indicator. Finally, the sizes of the anchor and label frames are adjusted according to the dimensioning of the tiny objects in the capacitance sample, and an appropriate aspect ratio is set to achieve the effective training of the network in the candidate area. Experiments show that the optimized Mask R-CNN network can achieve a defect detection rate that is above 98%.

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Funding

This study was supported by scientific research foundation of Nanjing Vocational University of Industry Technology (YK18-03-03) and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (20KJB410005).

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Correspondence to Cheng Cheng.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10878-024-01130-0

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Cheng, C., Dai, N., Huang, J. et al. RETRACTED ARTICLE: Capacitance pin defect detection based on deep learning. J Comb Optim 44, 3477–3494 (2022). https://doi.org/10.1007/s10878-022-00904-8

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  • DOI: https://doi.org/10.1007/s10878-022-00904-8

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