An Obstacle Detection Algorithm Suitable for Complex Traffic Environment

Abstract

For the task of obstacle detection in a complex traffic environment, this paper proposes a road-free space extraction and obstacle detection method based on stereo vision. The proposed method combines the advantages of the V-disparity image and the Stixel method. Firstly, the depth information and the V-disparity image are calculated according to the disparity image. Then, the free space on the road surface is calculated through the RANSAC algorithm and dynamic programming (DP) algorithm. Furthermore, a new V-disparity image and a new U-disparity image are calculated by the disparity image after removing the road surface information. Finally, the height and width of the obstacles on the road are extracted from the new V-disparity image and U-disparity image, respectively. The detection of obstacles is realized by the height and width information of obstacles. In order to verify the method, we adopted the object detection benchmarks and road detection benchmarks of the KITTI dataset for verification. In terms of the accuracy performance indicators quality, detection rate, detection accuracy, and effectiveness, the method in this paper reaches 0.820, 0.863, 0.941, and 0.900, respectively, and the time consumption is only 5.145 milliseconds. Compared with other obstacle detection methods, the detection accuracy and real-time performance in this paper are better. The experimental results show that the method has good robustness and real-time performance for obstacle detection in a complex traffic environment.

Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

“…According to the principle of binocular ranging, in an ideal state, the parallax of the image we obtain is inversely proportional to the depth information (distance), with the parallax of the image represented by d [26]. According to the principle of triangle similarity, we can obtain the relationship shown in Formulas ( 7) and ( 8) and calculate the value of Z.…”

“…According to this conversion process, the conversion relationship of any object in space from the world coordinate system to the pixel coordinate system can be obtained, as shown in Formula (9). According to the principle of binocular ranging, in an ideal state, the parallax of the image we obtain is inversely proportional to the depth information (distance), with the parallax of the image represented by d [26]. According to the principle of triangle similarity, we can obtain the relationship shown in Formulas ( 7) and ( 8) and calculate the value of Z.…”

“…Schematic diagram of binocular positioning. In the figure, f is the focal length of the camera; point P is the target point in the physical world; b is the baseline; O R and O T are the optical centers of the left and right cameras; p and p ′ are the imaging points of point P in the left and right camera image coordinate systems, respectively; Z is the distance between point P and the camera; x R and x T are the abscissas of the projection of point P in space in the images of the left and right cameras, respectively.According to the principle of binocular ranging, in an ideal state, the parallax of the image we obtain is inversely proportional to the depth information (distance), with the parallax of the image represented by d[26]. According to the principle of triangle similarity, we can obtain the relationship shown in Formulas (7) and (8) and calculate the value of Z.…”

A Novel Method for Obstacle Detection in Front of Vehicles Based on the Local Spatial Features of Point Cloud

“…This method lifted the constraint of the stixel to touch the ground surface and allowed for multiple stixels along every column of the image, thus enabling a more comprehensive description of the traffic scene. Reference [5] proposed an obstacle detection method combining the v-disparity image and stixel method. This method computationally reduced the complexity of making an effective Hough space and improved the accuracy of the road surface extraction results.…”

“…In the field of the environment perception of automobile driving assistance systems, there are currently two solutions in the industry [4]; namely, machine vision solution [5,6] and radar solution [7,8]. Both solutions have their own advantages, but the machine vision solution is closer to the human driving form and has significant potential for development.…”