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Safety helmet wearing status detection based on improved boosted random ferns

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Abstract

The safety helmet wearing of workers is extremely important to their safety in construction scenarios, and it is very meaningful for computer vision, pattern recognition and artificial intelligence. This paper proposes a new Improved Boosted Random Ferns algorithm (IBRFs) for safety helmet wearing status detection. IBRFs originates from the Boosted Random Ferns algorithm (BRFs) and introduces the weighted coefficient to improve. In IBRFs, firstly, the feature is extracted by Histogram of Oriented Gradient (HOG) to form the feature domain space of the image. Secondly, the random binary test method is used to construct random ferns in the feature domain space. Then, a weak (acceptable) classifier is constructed by random ferns. Finally, an improved Real AdaBoost algorithm is used to select the most discriminative ones to construct IBRFs. Experimental evaluation on an enlarged public Safety Helmet Wearing-datasets (GZMU-SHWD) shows that the result of IBRFs outperforms those of the existing advanced detection algorithms, including SSD, YOLOv3 and Faster R-CNN, which further demonstrates the effectiveness of IBRFs for safety helmet wearing status detection.

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Notes

  1. The SWHD database: [Online]. Available: https://github.com/njvisionpower/Safety-Helmet-Wearing-Dataset#model, October 11, 2019.

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Acknowledgments

The work is supported by National Natural Science Foundation of China (61802082, 61762020, 61263034), Guizhou Provincial Department of Education (Qian Jiao He KY ZI [2018] 018) .

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Authors and Affiliations

  1. School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, 550025, China

    Shiqin Yue, Qian Zhang, Dingqin Shao, Yu Fan & Jinhua Bai

  2. Academic Affairs Office, Guizhou Minzu University, Guiyang, 550025, China

    Qian Zhang

  3. Key Laboratory of Pattern Recognition and Intelligent Systems of Guizhou Province, Guiyang, 550025, China

    Shiqin Yue, Dingqin Shao, Yu Fan & Jinhua Bai

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  1. Shiqin Yue
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  2. Qian Zhang
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  4. Yu Fan
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  5. Jinhua Bai
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Correspondence to Qian Zhang.

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Yue, S., Zhang, Q., Shao, D. et al. Safety helmet wearing status detection based on improved boosted random ferns. Multimed Tools Appl 81, 16783–16796 (2022). https://doi.org/10.1007/s11042-022-12014-y

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