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Small obstacles image detection and classification for driver assistance

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Abstract

Small obstacles can cause big accidents, even if the vehicle is equipped with an intelligent auxiliary system. In order to detect four kinds of small obstacles quickly and accurately, this paper proposes an optimized neural network algorithm based on YOLOv3. K-Means+ is used to determine the prior box and enhance the adaptability of the YOLO scale. For the data samples imbalance, loss function of YOLO is improved to increase the precision of the prediction box. In addition, a special classification and counting algorithm is proposed to get results quickly and visually. The experimental results show that the our method can classify and locate four kinds of small obstacles more accurately and faster.

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Authors and Affiliations

  1. Fujian University of Technology, School of Electronic, Electrical Engineering and Physics, University Town, No33 Xueyuan Road, Minhou, Fuzhou, China

    Binghuang Chen & Xin Yang

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  1. Binghuang Chen
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  2. Xin Yang
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Correspondence to Binghuang Chen.

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Chen, B., Yang, X. Small obstacles image detection and classification for driver assistance. Multimed Tools Appl 81, 30785–30795 (2022). https://doi.org/10.1007/s11042-022-12706-5

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  • DOI: https://doi.org/10.1007/s11042-022-12706-5

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