Gravel automatic sieving method fusing macroscopic and microscopic characteristics

Abstract

Measuring the grain size distribution (GSD) of unconsolidated particles is critical to understanding coastal spreading, riverbed dynamics, and sediment transport. The current study presents a novel gravel automatic sieving (GAS) method designed to improve the accuracy and reliability of particle size analyses. At the macroscopic, the method utilizes the convex hull property of gravel to define the maximum extent of the searched gravel, effectively reducing over and under-segmentation problems. At the microscopic, the accuracy of gravel segmentation is improved by analyzing the color space characteristics of gravel to identify the pixel patches of gravel accurately. To validate the effectiveness of the GAS method, the proposed method was tested in both the laboratory and the field. In the laboratory, four artificial samples were processed using the GAS method, and the results were compared with those obtained using the traditional sieving method. The results showed that the correlation coefficients between the GAS method and the traditional sieving method ranged from 94.3% to 97.8%, and the relative errors ranged from 5.8% to 20.9%, demonstrating the validity of the GAS method. In addition, the application of ImageJ software to manually identify the particle size method (ImageJ method) was also compared with the mechanical sieving method, and the correlation coefficient between the two methods was greater than 98.2%, and the relative error was less than 10.9%, so the ImageJ method can be used as a standardized method to measure the other methods. In the field, sixteen images taken in four different regions and at different times were analyzed using the ImageJ method as a benchmark. The performance of the automatic with image filtering (AIF), BASEGRAIN, and the GAS methods also were compared. The results show that the relative errors range from 28.1% to 94.6% for the BASEGRAIN, 16.8% to 1003.6% for the AIF method, and only 5.6% to 30.7% for the GAS method. As a result, the GAS method demonstrates higher accuracy and stability in complex environments.