Abstract:Aiming at the problems of insufficient feature extraction in stereo matching algorithms in traditional obstacle detection, high mismatch rates in areas such as complex scenes and obvious lighting changes, and low accuracy of disparity maps obtained by the algorithm, a multi-scale based Stereo matching method of convolutional neural network is proposed. First, in the stage of calculating the matching cost, a multi-scale convolutional neural network model is established, and the multi-scale convolutional neural network is used to capture the multi-scale features of the image. In order to enhance the model"s anti-interference and fast convergence capabilities, improvements are proposed in the original loss function, so that the new loss function can be trained simultaneously with two positive and one negative samples during training, which shortens the model training time. Secondly, in the cost aggregation stage, a global energy function is constructed to decompose the optimal problem on a two-dimensional image into a one-dimensional problem in four directions. Using the idea of ??dynamic programming, the optimal parallax is obtained. Finally, the obtained parallax is further refined through left-right consistency detection to obtain a final parallax map. A comparison experiment was performed on the standard stereo matching image test pair provided by the Middlebury dataset. The average error matching rate of the algorithm verified by the experiment was 4.94%, which is less than the comparison experiment results. The accuracy of matching in obvious illumination changes and complex regions is improved. A high-precision parallax map was obtained through experiment.
