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An object detection method for the work of an unmanned sweeper in a noisy environment on an improved YOLO algorithm

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Abstract

Efficient and accurate object detection is crucial for the widespread use of low-cost unmanned sweepers. This paper focuses on the low-cost sweeper in practical working scenarios and proposes a traffic participant detection method based on an enhanced YOLO-v5 model. To train the model on noise knowledge, three types of noise are added to the data set in the offline phase, according to the vibration response of the mathematical model, and the impact of the low-cost camera. The loss function was optimized to balance detection accuracy and real-time performance while focusing on traffic participant detection using YOLO-v5. CTDS and BFSA modules were proposed based on the attention mechanism to enhance the YOLO-v5 model. Comparative experiments demonstrated the effectiveness of the proposed method, with the enhanced YOLO-v5 model achieving a 4.5% higher mean average precision than the traditional YOLO-v5 network. Moreover, the proposed method can process images at a frame per second of 89 while ensuring real-time performance, meeting the object detection requirements of actual sweeper.

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Data availability

Due to the nature of this study and in order to protect the privacy of study participants, participants of this study did not agree for their data to be shared publicly, so supporting data are not available.

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510641, China

    Junliang Huo, Baijun Shi & Yihuai Zhang

  2. Intelligent Transportation Thrust, Systems Hub, The HongKong University of Science and Technology(Guangzhou), Guangzhou, 511458, China

    Yihuai Zhang

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  1. Junliang Huo
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  2. Baijun Shi
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  3. Yihuai Zhang
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Material preparation, data collection, analysis and modification, experiment were performed by JH, the first draft of the manuscript was written by JH, All authors read and approved the final manuscript.

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Correspondence to Baijun Shi.

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Huo, J., Shi, B. & Zhang, Y. An object detection method for the work of an unmanned sweeper in a noisy environment on an improved YOLO algorithm. SIViP 17, 4219–4227 (2023). https://doi.org/10.1007/s11760-023-02654-4

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