Image Road Surface Classification Based on GLCM Feature Using LGBM Classifier
Authors:
- RobetRORobet
- AndiANAndi
- HendriHEHendri
Open Access
- 1 September 2022
- conference paper
- Published by IOP Publishing in IOP Conference Series: Earth and Environmental Science
- Vol. 1083 (1)
Abstract
The road is a land transportation infrastructure that is often used by the community to support their daily activities. Damaged road conditions will cause inconvenience and especially safety for users. Therefore, road repairs must be carried out immediately. The problem currently being faced by the public works services is that identifying road damage is still done manually, so it takes a long time and costs, therefore the approach can be taken is using a computer to do texture feature extraction on road surface images to obtain information using the GLCM method and classify using the LGBM method for normal, cracked, potholed road surfaces with an accuracy of 90%.Keywords
This publication has 14 references indexed in Scilit:
- Fast and accurate computation of orthogonal moments for texture analysisPattern Recognition, 2018
- Defect detection of bamboo strips based on LBP and GLCM features by using SVM classifier2018
- International Conference on Recent Trends in Physics 2016 (ICRTP2016)Journal of Physics: Conference Series, 2016
- Myanmar paper currency recognition using GLCM and k-NN2016
- Real-time Road Congestion Detection Based on Image Texture AnalysisProcedia Engineering, 2016
- Mammograms Classification Using Gray-level Co-occurrence Matrix and Radial Basis Function Neural NetworkProcedia Computer Science, 2015
- Image-Based Road Type Classification2014
- Multi-modal Bike Sensing for Automatic Geo-annotation - Geo-annotation of Road/Terrain Type by Participatory Bike-sensing2013
- GLCM and Fuzzy Clustering for Ocean Features Classification2010
- Textural Features for Image ClassificationIEEE Transactions on Systems, Man, and Cybernetics, 1973