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An Improved YOLOX for Detection in Urine Sediment Images

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Intelligent Robotics and Applications (ICIRA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13458))

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Abstract

In clinical medicine, the detection of human urine sediment is usually a basic test. The indicators of test can effectively analyze whether the patient has the disease. The traditional method of chemical, physical and microscopic analysis of samples artificially is time-consuming and inefficient. With the development of deep learning technology, many detectors now can replace traditional manual work and play a good detection effect. So it is of great value to put deep learning technology into the medical field. Usually, the Urine Microscopic Image has challenges for research, in which many detectors can not detect the cells well due to their small scale and heavy overlap occlusion. Therefore, we propose a novel detector for the detection of urine sediment in this paper. Firstly, considering the friendliness of YOLOX to the small objects, we adopt the framework from the YOLOX. Secondly, we add spatial, channel and position attention to enhance the feature information to achieve more accurate detection results. Then, the better Giouloss is also applied to make a better regression of the bounding box. Finally, the experimental results show that our improved model based on YOLOX achieves \(44.5\%\) \(A P_{50-90}\) and \(80.1\%\) \(A P_{50}\) on the public dataset Urine Microscopic Image Dataset, which is far better than other detectors.

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Acknowledgments

This work was partly supported by the National Natural Science Foundation of China (No. 61873240) and the Foundation of State Key Laboratory of Digital Manufacturing Equipment and Technology (Grant No. DMETKF2022024).

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Minming Yu, Yanjing Lei, Wenyan Shi, Yujie Xu & Sixian Chan

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  1. Minming Yu
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  2. Yanjing Lei
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  3. Wenyan Shi
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  4. Yujie Xu
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  5. Sixian Chan
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Corresponding author

Correspondence to Yanjing Lei .

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Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institute of Advanced Technology, Shenzhen, China

    Xinyu Wu

  7. Harbin Institute of Technology, Shenzhen, China

    Weihong Ren

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Yu, M., Lei, Y., Shi, W., Xu, Y., Chan, S. (2022). An Improved YOLOX for Detection in Urine Sediment Images. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13458. Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_50

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  • DOI: https://doi.org/10.1007/978-3-031-13841-6_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13840-9

  • Online ISBN: 978-3-031-13841-6

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