Preceding Vehicle Detection Method Based on Visual Fusion

Abstract

When the general preceding vehicle detection is carried for monocular vision, the accuracy of the detection is affected by the dynamic background, illumination and occlusion. This paper proposed a preceding vehicle detection method fusing monocular and binocular vision method. Firstly, the potential area of the preceding vehicle was established by using SSD (Single Shot Multibox Detection) algorithm, then, the binocular vision-based vehicle detection method for the potential area was used, its U-V disparity was calculated, and the accurate preceding vehicle position by the vehicle depth information in the image was obtained; finally, the missed detection and false detections in the monocular vision detector were removed with the U-V disparity detections obtained by binocular vision detector. And the final results combined with the type and position information were outputted. Thereby the accuracy of the preceding vehicle detection was improved. The test result showed that on the condition of KITTI date set the visual fusion method can effectively improve the accuracy of vehicle detection. And compared with SSD algorithm in monocular, the Detection Rate was increased by 1.46%, the Recall Rate was increased by 2.83%, the Missed Rate was reduced by 2.83% and the False Rate was reduced by 1.10% than the monocular SSD algorithm.