Three-dimensional Characterization of Road Aggregates Based on Thermal Infrared Depth Estimation

To optimize and realize the rapid identification of three-dimensional features of road aggregates in complex environments, in this study, an infrared image three-dimensional reconstruction method is proposed based on temperature-assisted enhancement of aggregate profile. First, a 3D model library is reconstructed by calculating the point-cloud data of the aggregates with different key sieve holes. On this basis, the particle size characteristics of the 3D model, OBB enveloping box characteristics, and recognition of aggregate pinflake particles are evaluated and analyzed. The results show that the correlation coefficient between the 3D model particle size and aggregate particle size reaches 0.9916, and the OBB scale feature can reflect the 3D size information of the aggregate with a small fitting error. Compared with the traditional two-dimensional identification method, the accuracy of identifying needle flake particles based on the OBB scale feature is improved by 12%, providing new ideas for the practical application of road aggregate feature recognition.