In the motor vehicle inspection scenario, accurate identification and positioning of vehicle nameplates is an important prerequisite for automated robotic inspection operations. In view of the current problems that vehicle nameplates are mainly checked manually and the level of intelligence is low, a target recognition and localisation method based on binocular vision combined with improved SURF algorithm is proposed. Firstly, feature points are extracted based on the SURF algorithm, and target recognition is achieved by combining the nearest neighbour ratio method with the RANSAC algorithm based on the gradient constraint of the parallax; then the edge contour of the target in the template image is obtained based on the Canny edge detection method, and the target shape centre on the template image is obtained based on the contour, and the left and right scene map target shape centres are obtained using the single-response matrix mapping; finally, target localisation is achieved by combining the principle of binocular vision 3D reconstruction. The experimental results show that the method has good recognition efficiency and accuracy, and can successfully identify and locate the target, which has certain application value.
Stereo matching is a critical step in three-dimensional reconstruction. The conventional Census transform is excessively dependent on the central pixel and is susceptible to noise interference. Therefore, this study proposes a cost calculation method for multi-feature fusion based on the HSV color space and an improved Census transform. To address the problem of poor matching in regions with weak textures and depth discontinuities. In this study, a new window construction method is developed based on the length and variable color threshold to obtain the crossover region. After dividing the weights of the constructed windows, the cost aggregation is calculated. Finally, a dense parallax map is obtained via parallax calculation and optimization. Experiments were conducted based on the data provided by the Middlebury dataset. The experimental results showed that the improved cost calculation algorithm was significantly resistant to noise, and the average mis match rate of the algorithm was reduced to 8.03%.
An improved ORB feature point extraction method is proposed for the disadvantages of concentrated extraction, uneven distribution of feature points, slow matching speed and high matching error rate due to the fixed threshold value set by the traditional ORB algorithm in feature point extraction. Firstly, the adaptive threshold is set by automatically calculating the greyscale value around the image block, and the feature points are judged and extracted using this threshold; the traditional ORB algorithm uses the greyscale center of mass method to determine the principal direction, but its accuracy has certain deviations, and the accuracy of calculating the principal direction is improved by Gaussian weighting the pixel greyscale values and then calculating the principal direction by the greyscale center of mass method. The experimental results show that the improved ORB algorithm has a larger number of feature points extracted, a more even distribution and a higher correct matching rate.
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