TY - GEN
T1 - Road defect classification using Gray Level Co-Occurrence Matrix (GLCM) and Radial Basis Function (RBF)
AU - Pramestya, Ravy Hayu
AU - Sulistyaningrum, Dwi Ratna
AU - Setiyono, Budi
AU - Mukhlash, Imam
AU - Firdaus, Zaimatul
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/13
Y1 - 2018/11/13
N2 - The road is an important infrastructure, so it is necessary to maintain the road periodically. Currently, the road defect assessment is still manual. Unfortunately, this method takes a long time and can cause ambiguity because of the subjectivity factor. Along with the development of science on image processing technology and machine learning, assessment of road defects can be done automatically by the machine. Road defect classification is the first step in automated road assessment. The image of road defect will be taken from the machine, taking on the features of each defect and classifying the image of its features. GLCM is a feature extract method that has been widely used for image processing. This study classifies some types of road defects, ie potholes, cracks and other defects using the Gray Level Co-occurrence Matrix (GLCM) as a feature extract, while Radial Basis Function (RBF) as an object classification. The proposed method can classify defects with an average of 93% accuracy, 93% precision and 100% recall.
AB - The road is an important infrastructure, so it is necessary to maintain the road periodically. Currently, the road defect assessment is still manual. Unfortunately, this method takes a long time and can cause ambiguity because of the subjectivity factor. Along with the development of science on image processing technology and machine learning, assessment of road defects can be done automatically by the machine. Road defect classification is the first step in automated road assessment. The image of road defect will be taken from the machine, taking on the features of each defect and classifying the image of its features. GLCM is a feature extract method that has been widely used for image processing. This study classifies some types of road defects, ie potholes, cracks and other defects using the Gray Level Co-occurrence Matrix (GLCM) as a feature extract, while Radial Basis Function (RBF) as an object classification. The proposed method can classify defects with an average of 93% accuracy, 93% precision and 100% recall.
KW - Gray Level Co-Occurrence Matrix (GLCM)
KW - Radial Basis Function (RBF)
KW - Road Defect
UR - http://www.scopus.com/inward/record.url?scp=85058382701&partnerID=8YFLogxK
U2 - 10.1109/ICITEED.2018.8534769
DO - 10.1109/ICITEED.2018.8534769
M3 - Conference contribution
AN - SCOPUS:85058382701
T3 - Proceedings of 2018 10th International Conference on Information Technology and Electrical Engineering: Smart Technology for Better Society, ICITEE 2018
SP - 285
EP - 289
BT - Proceedings of 2018 10th International Conference on Information Technology and Electrical Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Information Technology and Electrical Engineering, ICITEE 2018
Y2 - 24 July 2018 through 26 July 2018
ER -