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Video detection of foreign objects on the surface of belt conveyor underground coal mine based on improved SSD

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Abstract

Aiming at the problem of belt conveyor damage caused by the presence of foreign objects on the belt conveyor in coal mines, this paper proposed that video detection of foreign objects on the belt surface was performed based on SSD. Improvements on SSD network are made from following aspects. Firstly, the deep separable convolution method is used to reduce the amount of parameters in the SSD algorithm and improve the speed. Then, GIOU loss function is adopted instead of the position loss function in the original SSD to improve the detection accuracy. Finally, the extracted position of the feature map and the proportion of the default boxes are optimized to improve the detection accuracy. The experiment results show that the improved algorithm proposed in this paper is superior to the original SSD algorithm, the average accuracy rate has been increased from 87.1 to 90.2%, and the detection frame rate has been increased from 32 to 41 FPS.

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Acknowledgements

This work (Grants 61703329) was supported by National Natural Science Foundation of China and National Key Research and Development Program of Shaanxi Province, China (2019KW-046).

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Authors and Affiliations

  1. Xi’an University of Science and Technology, Xi’an, China

    Yuanbin Wang, Yujing Wang & Langfei Dang

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  1. Yuanbin Wang
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  2. Yujing Wang
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  3. Langfei Dang
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Correspondence to Yujing Wang or Langfei Dang.

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Wang, Y., Wang, Y. & Dang, L. Video detection of foreign objects on the surface of belt conveyor underground coal mine based on improved SSD. J Ambient Intell Human Comput 14, 5507–5516 (2023). https://doi.org/10.1007/s12652-020-02495-w

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