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Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

Wang, Hai ; Lou, Xinyu ; Cai, Yingfeng ; Li, Yicheng ; Chen, Long Lloret, Jaime ; Jaime Lloret

Journal of sensors, 2019-01, Vol.2019, p.1-9 [Periódico revisado por pares]

New York: Hindawi

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  • Título:
    Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion
  • Autor: Wang, Hai ; Lou, Xinyu ; Cai, Yingfeng ; Li, Yicheng ; Chen, Long
  • Lloret, Jaime ; Jaime Lloret
  • Assuntos: Algorithms ; Autonomous vehicles ; Barriers ; Classification ; Hypotheses ; International conferences ; Lidar ; Machine learning ; Neural networks ; Obstacle avoidance ; Pattern recognition ; Real time ; Target detection ; Target recognition ; Vision
  • É parte de: Journal of sensors, 2019-01, Vol.2019, p.1-9
  • Descrição: Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.
  • Editor: New York: Hindawi
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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