By the end of 2019, the total number of motor vehicles in China had reached 340 million, of which 250 million were cars. In order to reduce the occurrence of road traffic accidents, more and more automobile manufacturers have begun to develop vehicle-mounted image-assisted driving technology based on dashcam. The most important part of the collision avoidance system is its visual recognition and ranging. Therefore, it is of great significance to study the range finder system based on vision. This paper builds a 3D simulation simulator based on Unreal Engine and uses specific data sets to test and verify various classic algorithms of vision-based identification and ranging. According to different operating conditions, the advantages and disadvantages of each algorithm are listed and summarized, and the limitations of their application are pointed out. As for the detection part, this paper conducts experimental verification on SVM+HOG and LBP+Cascade detection algorithms to obtain their recognition accuracy, positioning accuracy and calculation time information, and analyzes their advantages and disadvantages. As for the ranging part, this paper conducts ranging tests on monocular and binocular ranging algorithms, obtains ranging errors at different positions and analyzes the advantages and disadvantages of different algorithms. Experimental results show that the monocular distance requires a more accurate identification frame than the binocular distance measurement. Binocular distance measurement can measure the distance information of the target more quickly and accurately when the identification frame is very small.
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