A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
PREDICTING RAILROAD BALLAST FOULING CONDITIONS BASED ON BALLAST IMAGE
Evaluating railway ballast fouling condition is critical to assessing track conditions and arranging proper ballast maintenance. Because fouled ballast materials with different fouling conditions have different material properties, these properties can be used to evaluate the fouling severity. Various previously developed approaches to estimating fouling conditions often require special sensors or equipment, and well-trained technicians. Recently, convolutional neural network (CNN) based computer vision approaches have performed particle segmentation to obtain ballast grain size distribution. While the coarse aggregate fraction can be evaluated, many such approaches do not segment fine particles. This disclosure is an image analysis approach to directly estimate the ballast fouling conditions. First, fouled ballast images with different fouling conditions are taken as the reference. Then, the RGB color distributions of the fouled ballast images are processed through statistical analysis. A strong linear correlation between Fouling Index (FI) and Variance is found and used to establish an FI prediction model which has been tested and validated by additional fouled ballast samples.
PREDICTING RAILROAD BALLAST FOULING CONDITIONS BASED ON BALLAST IMAGE
Evaluating railway ballast fouling condition is critical to assessing track conditions and arranging proper ballast maintenance. Because fouled ballast materials with different fouling conditions have different material properties, these properties can be used to evaluate the fouling severity. Various previously developed approaches to estimating fouling conditions often require special sensors or equipment, and well-trained technicians. Recently, convolutional neural network (CNN) based computer vision approaches have performed particle segmentation to obtain ballast grain size distribution. While the coarse aggregate fraction can be evaluated, many such approaches do not segment fine particles. This disclosure is an image analysis approach to directly estimate the ballast fouling conditions. First, fouled ballast images with different fouling conditions are taken as the reference. Then, the RGB color distributions of the fouled ballast images are processed through statistical analysis. A strong linear correlation between Fouling Index (FI) and Variance is found and used to establish an FI prediction model which has been tested and validated by additional fouled ballast samples.
PREDICTING RAILROAD BALLAST FOULING CONDITIONS BASED ON BALLAST IMAGE
GONG YUFENG (author) / QIAN YU (author)
2024-02-15
Patent
Electronic Resource
English
Identification of Railroad Ballast Fouling through Particle Movements
| British Library Online Contents | 2018