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An improved SegNet network model for accurate detection and segmentation of car body welding slags

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Abstract

The prerequisite for realizing robotic self-adaptive grinding of the car body welding slags is to accurately identify the small and randomly distributed welding slags, and to segment their contour from the complex background. In this paper, an improved SegNet network model is proposed to address the challenging problems in small target detection and contour extraction. Both the ability to capture image features and to perceive global and local information is enhanced by adding the context extractor into the SegNet network. Furthermore, the residual structure and BN layer are used to optimize the decoder of the SegNet network, and the Dropout layer is employed to effectively avoid overfitting of the training model. Based on these strategies, the adaptability of the network to multi-scale targets is further enhanced. Experiments on car body welding slags detection indicate that the accuracy rate of the improved SegNet network model can reach 99.5%, which is 2.7% higher than that before improvement. Meanwhile, the boundary pixels at connection area among welding slags, metal plate, and background are accurately segmented, which provides data support for the subsequent robotic grinding of car body welding slags.

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The data and materials of this manuscript are available from the corresponding authors on reasonable request.

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Funding

This study was funded by the National Natural Science Foundation of China (No. 51975443), the Hubei Province Key R&D Program (No. 2020BAA025) and the “111” Project (No. B17034).

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Authors and Affiliations

  1. Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan, 430070, China

    Dahu Zhu, Chen Qian, Chao Qu, Minqi He, Shuwen Zhang, Qiuping Tu & Wenting Wei

  2. Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, 430070, China

    Dahu Zhu, Chen Qian, Chao Qu, Minqi He, Shuwen Zhang, Qiuping Tu & Wenting Wei

Authors
  1. Dahu Zhu
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  2. Chen Qian
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  3. Chao Qu
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  4. Minqi He
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  5. Shuwen Zhang
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  6. Qiuping Tu
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  7. Wenting Wei
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Contributions

Dahu Zhu: conceptualization, funding acquisition, writing. Chen Qian: methodology, investigation, software. Chao Qu: methodology, validation, writing. Minqi He: writing — reviewing and editing. Shuwen Zhang: investigation, software. Qiuping Tu: methodology, investigation, software. Wenting Wei: methodology, writing — reviewing and editing.

Corresponding author

Correspondence to Wenting Wei.

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Zhu, D., Qian, C., Qu, C. et al. An improved SegNet network model for accurate detection and segmentation of car body welding slags. Int J Adv Manuf Technol 120, 1095–1105 (2022). https://doi.org/10.1007/s00170-022-08836-7

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  • DOI: https://doi.org/10.1007/s00170-022-08836-7

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