Key points are not available for this paper at this time.
In the process of using binocular vision for ranging, target detection and image matching are the key to the ranging process. To address the problems of low target detection accuracy and high distance ranging error in traditional binocular ranging methods, this article proposes an improved binocular vision ranging algorithm based on YOLOv5. First, the binocular camera is calibrated by the checkerboard calibration method, and the imaging plane of the binocular stereo vision is corrected to the ideal structure by the epipolar correction algorithm. Then, the target is detected by the improved YOLOv5 algorithm. This method uses the SimOTA label allocation strategy to further reduce the training time and computational complexity of the model and introduces L ₄₈₎ₔ to solve the problem of the unclear definition of the length–width ratio in the original L ₂₈₎ₔ, further improving the speed and accuracy of convergence. Moreover, focal loss is added to compensate for the imbalanced contribution of high- and low-quality samples in the gradient. Next, using the improved multiscale stereo matching algorithm, the speed of the matching algorithm in large images is enhanced. After the initial matching point pairs have been obtained, the quadratic surface fitting method is used to obtain the subpixel disparity. The depth value of the target center point is obtained by conversion from the 2-D pixel coordinate system to the 3-D space coordinate system. A ranging experiment was carried out in the range of 20–200 m. The mean absolute error (MAE) index of the ranging result of the proposed method is only 2. 85 m, which verifies the effectiveness of the improved algorithm in both its theoretical and experimental aspects.
Wei et al. (Wed,) studied this question.