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Vehicle-Related Distance Estimation Using Customized YOLOv7

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Image and Vision Computing (IVCNZ 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13836))

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Abstract

With the popularity of autonomous driving, the development of ADAS (Advanced Driver Assistance Systems), especially collision avoidance systems, has become an important branch in the field of deep learning. In the face of complex traffic environments, collision avoidance systems need to detect vehicles quickly and accurately in traffic distance to the vehicle in front. Against this background, in this paper, we aim at investigating how to build a fast and robust model for vehicle distance estimation. The theoretical insights are synthesized in the context of odometry and customized YOLOv7 based on what a conceptual framework is proposed. In this paper, KITTI is employed as the dataset for model training and testing. Being one of the pioneer works on distance estimation based on KITTI, the unique value of this research work lies in the first time using YOLOv7 with attention model as a distance estimation model and getting 4.253 on RMSE.

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Authors and Affiliations

  1. Auckland University of Technology, Auckland, 1010, New Zealand

    Xiaoxu Liu & Wei Qi Yan

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  1. Xiaoxu Liu
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  2. Wei Qi Yan
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Correspondence to Xiaoxu Liu .

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Editors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

  2. Auckland University of Technology, Auckland, New Zealand

    Minh Nguyen

  3. Auckland University of Technology, Auckland, New Zealand

    Martin Stommel

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Liu, X., Yan, W.Q. (2023). Vehicle-Related Distance Estimation Using Customized YOLOv7. In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_7

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  • DOI: https://doi.org/10.1007/978-3-031-25825-1_7

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