Algorithm for Measuring the Outer Contour Dimension of Trucks Using UAV Binocular Stereo Vision

Promoting the management of the over-limit of freight transport vehicles plays an important role in the sustainable development of the highway industry. Vehicle outer contour dimension measurement is a key element in highway over-limit detection. The current detection approaches and research methods, however, are insufficient for high-precision flow detection. Therefore, this study proposes an algorithm for measuring the dimensions of a truck's outer contours, using unmanned aerial vehicle (UAV) binocular stereo vision. First, this study leverages a binocular camera mounted on a UAV to reconstruct the 3D point clouds of the truck. Second, the point cloud data are clustered using an FoF (Friends-of-Friends algorithm); this recognizes the cluster of truck points according to the truck's characteristics. Finally, the principal component analysis and the Gaussian kernel density estimation are used to generate the outer contour dimensions of the trucks. Twenty model vehicles are selected as test objects to verify the reliability of the algorithm. The average error of the algorithm is represented by calculating the average value of the difference between the real size and the predicted size of the three dimensions. The experimental results demonstrate that the average error of this measurement approach is less than 2.5%, and the method is both stable and robust. This approach aligns with national regulations for over-limit detection.