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Casting-DETR: An End-to-End Network for Casting Surface Defect Detection

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Abstract

The task of utilizing machine vision for the detection of casting surface defects is characterized by small targets, real-time performance, and ease of mobility. The direct application of current mainstream object detection networks for defect detection presents issues of low accuracy and efficiency. Consequently, in this paper, we introduce Casting-DETR, an end-to-end network designed for casting surface defect detection. To assess and validate the model’s performance, 554 images of casting samples with surface defects were employed. Casting-DETR achieved an impressive detection rate of 98.97% on the test set, with a single image detection time of 91.5ms. Furthermore, a real-time detection system, built using PyQT6, was tested in four different environments. Casting-DETR exhibited exceptional performance, maintaining a single-frame detection time of approximately 90 ms, demonstrating the model’s high robustness and suitability for real-time detection. The Casting-DETR network proposed in this paper is an end-to-end solution with rapid convergence, superior detection accuracy, and swift detection speeds, offering a fresh perspective for similar detection tasks within the industry.

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Funding

This work was supported by [China National Key Research and Development Project](Grant Number [2017YFE0113200]), and by [Anhui University of Technology youth Fund] (Grant Number [QZ202217]), and by [Anhui Provincial Natural Science Foundation](Grant Number [2108085ME173]), and by [Open Project of China International Science and Technology Cooperation Base on Intelligent Equipment Manufacturing in Special Service Environment](Grant Numbers [ISTC2021KF07], [ISTC2021KF08] and [ISTC2022KF04]).

Author information

Author notes

  1. **ang-rong Xu, Long Zhang, Ju Gao, Aleksandar Rodić, Petar B. Petrovic, Hai-yan Wang, Shan-shan Xu and Zhi-xiong Wang have contributed equally to this work.

Authors and Affiliations

  1. School of Mechanical Engineering, Anhui University of Technology, Maxiang Road, Ma’anshan, 243032, Anhui Province, China

    Quan-cheng Pu, Hui Zhang, **ang-rong Xu & Ju Gao

  2. School of Metallurgical Engineering, Anhui University of Technology, Maxiang Road, Ma’anshan, 243032, Anhui Province, China

    Hui Zhang & Long Zhang

  3. Faculty of Mechanical Engineering, University of Belgrade, Kraljice Marije, Belgrade, 11120, Anhui Province, Serbia

    Aleksandar Rodić & Petar B. Petrovic

  4. School of Osaka Medical Engineering, Ma’anshan University, Huangchi Road, Ma’anshan, 243032, Anhui Province, China

    Hai-yan Wang & Shan-shan Xu

  5. School of Medicine, Osaka University, Yamadaoka, Suita, 565-0871, Osaka, Japan

    Zhi-xiong Wang

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  1. Quan-cheng Pu
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Contributions

All authors contributed to the study conception and design. The first draft of the manuscript was written by [Quancheng Pu] and all authors commented on previous versions of the manuscript. The images of self-built dataset were taken by [Hui Zhang]. [Quancheng Pu] and [Hui Zhang] are co-first authors. All authors read and approved the final manuscript.

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Correspondence to **ang-rong Xu or Long Zhang.

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Pu, Qc., Zhang, H., Xu, Xr. et al. Casting-DETR: An End-to-End Network for Casting Surface Defect Detection. Inter Metalcast (2024). https://doi.org/10.1007/s40962-023-01212-5

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