Deep Learning based Object Distance Measurement Method for Binocular Stereo Vision Blind Area

Visual field occlusion is one of the causes of urban traffic accidents in the process of reversing. In order to meet the requirements of vehicle safety and intelligence, a method of target distance measurement based on deep learning and binocular vision is proposed. The method first establishes binocular stereo vision model and calibrates intrinsic extrinsic and extrinsic parameters, uses Faster R-CNN algorithm to identify and locate obstacle objects in the image, then substitutes the obtained matching points into a calibrated binocular stereo model for spatial coordinates of the target object. Finally, the obstacle distance is calculated by the formula. In different positions, take pictures of obstacles from different angles to conduct physical tests. Experimental results show that this method can effectively achieve obstacle object identification and positioning, and improve the adverse effect of visual field blindness on driving safety.

@article{Zhang2018,
  title     = {Deep Learning based Object Distance Measurement Method for Binocular Stereo Vision Blind Area},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {9},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Jiaxu Zhang and Shaolin Hu and Haoqiang Shi},
  doi       = {10.14569/IJACSA.2018.090977},
  url       = {https://doi.org/10.14569/IJACSA.2018.090977}
}