| Title |
A Study on the Optimal Convolution Neural Network Backbone for Sinkhole Feature Extraction of GPR B-scan Grayscale Images |
| DOI |
https://doi.org/10.12652/Ksce.2024.44.3.0385 |
| Keywords |
싱크홀;지표투과레이더;B-스캔 회색조 이미지;매개변수;최적 컨볼루션 신경망;아키텍처 효율 지수 Shinkhole;Ground-penetrating radar;B-scan grayscale images;Parameters;Optimal convolution neural network;Architecture efficient index |
| Abstract |
To enhance the accuracy of sinkhole detection using GPR, this study derived a convolutional neural network that can optimally extract
sinkhole characteristics from GPR B-scan grayscale images. The pre-trained convolutional neural network is evaluated to be more than
twice as effective as the vanilla convolutional neural network. In pre-trained convolutional neural networks, fast feature extraction is
found to cause less overfitting than feature extraction. It is analyzed that the top-1 verification accuracy and computation time are different
depending on the type of architecture and simulation conditions. Among the pre-trained convolutional neural networks, InceptionV3
are evaluated as most robust for sinkhole detection in GPR B-scan grayscale images. When considering both top-1 verification accuracy
and architecture efficiency index, VGG19 and VGG16 are analyzed to have high efficiency as the backbone for extracting sinkhole feature
from GPR B-scan grayscale images. MobileNetV3-Large backbone is found to be suitable when mounted on GPR equipment to extract
sinkhole feature in real time. |