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RETRACTED ARTICLE: Application of deep learning to detect defects on the surface of steel balls in an IoT environment

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This article was retracted on 12 April 2023

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Abstract

Precision steel balls enable the bearings of machine parts to maintain their relative position, and they reduce operating time during relative movement, reduce friction force, improve efficiency and extend service life. Inspections are mainly conducted manually by eye, with classification of detected defects. Such visual inspections may cause accuracy imbalances and cost problems. For this reason, deep learning technology has been introduced into production lines. The innovation of the research presented here was to use an object-detection model to detect defects on the surface of steel balls to reduce manpower consumption and improve accuracy in detection of steel ball defects. The “You Only Look Once” algorithm (YOLOv5) was used, along with a fast-region convolutional neural network (R-CNN), finally obtaining an average recall rate of 99% and a detection accuracy rate of 93%. The results showed the YOLO algorithm to be more capable of judging good products. The contribution of this research, the efficiency of production lines can be enhanced through better detection of flaws on steel balls. The comparison showed that YOLO and Faster R-CNN are better than traditional IP detection, and the above results constitute an 80% improvement in the current detection system of the partner company.

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Authors and Affiliations

  1. School of Computer Engineering, Guangzhou City University of Technology, Guangzhou, 510800, China

    Lei Wang

  2. School of Information Technology, St. Paul University, 3500, Tuguegarao, Philippines

    Lei Wang

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  1. Lei Wang
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Correspondence to Lei Wang.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s11227-023-05274-4

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Wang, L. RETRACTED ARTICLE: Application of deep learning to detect defects on the surface of steel balls in an IoT environment. J Supercomput 78, 16425–16452 (2022). https://doi.org/10.1007/s11227-022-04516-1

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