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An object detection network for wear debris recognition in ferrography images

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Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

The intelligent recognition of wear debris in ferrography images is a great challenge. Aiming at the detection characteristics of multi-scale objects, small objects and overlapping objects in ferrography images, a real-time on-line object detection model combining feature fusion, self-attention mechanism and improved NMS is proposed. Based on YOLOv3, a fully convolutional network based on feature fusion is constructed, and a residual unit with self-attention mechanism and an object selection mechanism based on improved non-maximum suppression are introduced. The performance of the model is compared with three advanced object detection algorithms on a great number of ferrography images. The experimental results show that the model has high detection accuracy, short detection time and strong adaptability. The proposed model can be further developed and applied to fault diagnosis and condition monitoring of machinery and equipment in future.

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Acknowledgements

This work was supported by the key research and development plan of Shandong province (2018GGX105002) and the key research and development plan of Shandong province (2019JZZY020712).

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Authors and Affiliations

  1. School of Merchant Marine, Shanghai Maritime University, Shanghai, 201306, China

    Fengguang Jia & Haijun Wei

  2. School of Naval Architecture and Port Engineering, Shandong Jiaotong University, Weihai, 264209, China

    Fengguang Jia, Hongyuan Sun, Lei Song & Fulin Yu

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  1. Fengguang Jia
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  2. Haijun Wei
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  3. Hongyuan Sun
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  4. Lei Song
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  5. Fulin Yu
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Correspondence to Fengguang Jia.

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Technical Editor: Celso Kazuyuki Morooka.

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Jia, F., Wei, H., Sun, H. et al. An object detection network for wear debris recognition in ferrography images. J Braz. Soc. Mech. Sci. Eng. 44, 67 (2022). https://doi.org/10.1007/s40430-022-03375-4

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  • DOI: https://doi.org/10.1007/s40430-022-03375-4

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