Computer Vision for Particle Size Analysis of Coarse-Grained Soils
Sompote Youwai Parchya Makam
Particle size analysis (PSA) is a fundamental technique for evaluating the
physical characteristics of soils. However, traditional methods like sieving
can be time-consuming and labor-intensive. In this study, we present a novel
approach that utilizes computer vision (CV) and the Python programming language
for PSA of coarse-grained soils, employing a standard mobile phone camera. By
eliminating the need for a high-performance camera, our method offers
convenience and cost savings. Our methodology involves using the OPENCV library
to detect and measure soil particles in digital photographs taken under
ordinary lighting conditions. For accurate particle size determination, a
calibration target with known dimensions is placed on a plain paper alongside
20 different sand samples. The proposed method is compared with traditional
sieve analysis and exhibits satisfactory performance for soil particles larger
than 2 mm, with a mean absolute percent error (MAPE) of approximately 6%.
However, particles smaller than 2 mm result in higher MAPE, reaching up to 60%.
To address this limitation, we recommend using a higher-resolution camera to
capture images of the smaller soil particles. Furthermore, we discuss the
advantages, limitations, and potential future improvements of our method.
Remarkably, the program can be executed on a mobile phone, providing immediate
results without the need to send soil samples to a laboratory. This
field-friendly feature makes our approach highly convenient for on-site usage,
outside of a traditional laboratory setting. Ultimately, this novel method
represents an initial disruption to the industry, enabling efficient particle
size analysis of soil without the reliance on laboratory-based sieve analysis.
KEYWORDS: Computer vision, Grain size, ARUCO