Abstract
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.
Highlights
With the improvement of peoples living standards, the number of vehicles in China has continuously increased
In view of the accuracy, intelligence and real-time requirements of distance measurement for traffic obstacles, a new method combining deep learning and binocular vision is proposed in this paper
3) Obstacle Detection Model Based on Faster R-convolutional neural networks (CNNs): After the feature extraction and candidate region proposal have been performed on the input image, the candidate regions with different sizes are connected to the ROI pooling layer, and the feature maps of these candidate regions are converted into a fixed size
Summary
With the improvement of peoples living standards, the number of vehicles in China has continuously increased. The traditional stereo feature matching method is to find corresponding points in the image pair from features such as edges, colors, and textures in the image [2] These methods are computationally intensive, lacking in pertinence, and are prone to mis-matching unnecessary targets, which reduces the performance and accuracy of the algorithm. With the rapid development of deep learning in the field of image and video processing, the target detection algorithm has developed by leaps and bounds. In view of the accuracy, intelligence and real-time requirements of distance measurement for traffic obstacles, a new method combining deep learning and binocular vision is proposed in this paper. Binocular vision is combined with the deep learning detection algorithm to achieve the distance measurement of obstacle targets. It is verified by experiments that the algorithm can realize the distance measurement task
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