An on-board vision system is recognized as a promising tool for vehicle early warning and monitoring. Timely accurate estimation of vehicle speed is critical in allowing the on-board vision system to calculate the vehicle location, plan a driving path, and apply emergency brakes to avoid accidents. However, the scene images captured by the vision system always suffer from global motion blur, which causes great difficulty in precisely estimating vehicle speed. While extensive efforts have been focused on blurred image restoration and real-time driving speed estimation in highway scenarios, very limited work has addressed urban scenarios in which the vehicle speed is often less than 40 km h−1. In order to bridge this research gap, this study proposes a new method for real-time vehicle speed estimation. Firstly, the spectrum characteristics of blurred images at low vehicle speeds were investigated to determine the relationship between the direction and spacing of the spectrogram and vehicle motion parameters. Then, the blur-direction and blur-scale of the vehicle motion were analyzed by double Radon transform to develop a speed estimation model. Experimental evaluation results demonstrate that the proposed method was able to estimate vehicle speed in urban scenarios without updating the hardware of existing on-board vision systems. The estimation error was below 7.13% and the calculation efficiency of a single frame was 30 ms, both of which meet the practical application requirements of intelligent vehicles.
Current O-ring dimension measurement algorithms based on machine vision are mainly whole-pixel level algorithms, which have the disadvantage of a low measurement accuracy. In order to improve the stability and accuracy of O-ring dimension measurement, a sub-pixel edge detection algorithm based on cubic spline interpolation is proposed for O-ring dimension measurement. After image pre-processing of the O-ring graphics, the whole-pixel-level O-ring edges are obtained by using a noise-resistant mathematical morphology method, and then the sub-pixel edge contours are obtained using a sub-pixel edge detection algorithm based on cubic spline interpolation. Finally, the edge curve is fitted with the least squares method to obtain its inner and outer diameter as well as the size of the wire diameter. The experimental data show that the algorithm has a mean square error of 4.8 μm for the outer diameter and 0.18 μm for the wire diameter. The outer diameter error is kept within ± 100 μm and the wire diameter error can be kept within ±15 μm. Compared with the whole pixel algorithm, the measurement accuracy has been greatly improved.
Aiming at solving the problem of manually measuring the fabric surface thickness, this paper proposes a three-dimensional (3D) reconstruction method based on the tangential two-dimensional (2D) sequence images. Firstly, the characteristic region of the fabric surface is extracted. Secondly, the image is splitting based on the maximum between-class variance method. Thirdly, the splitting image is processed by the morphological method. Fourthly, the canny operator is used to obtain the edge detection for calculating the edge contour coordinate. Finally, the surf function is used to reconstruct the 3D model of the fabric surface. To evaluate the performance of the proposed 3D model, the thickness and relief degree of the fabric surface are used, and the comparison between the proposed method and the manual measurement is carried out. The results demonstrate that, under a reasonable relief degree condition, the proposed method is more effective to evaluate the thickness of the fabric surface and the estimated thickness is more accurate than the manually measured one.
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