Three combination value of extraction features on GLCM for detecting pothole and asphalt road
The rate of vehicle accidents in various regions is still high accidents caused by many factors, such as driver negligence, vehicle damage, and road damage. However, transportation technology developed very rapidly, for example, a smart car. The smart car is land transportation that does not use hum...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Diponegoro University
2021-01-01
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| Series: | Jurnal Teknologi dan Sistem Komputer |
| Subjects: | |
| Online Access: | https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13828 |
| _version_ | 1797288676131078144 |
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| author | Yoke Kusuma Arbawa Fitri Utaminingrum Eko Setiawan |
| author_facet | Yoke Kusuma Arbawa Fitri Utaminingrum Eko Setiawan |
| author_sort | Yoke Kusuma Arbawa |
| collection | DOAJ |
| description | The rate of vehicle accidents in various regions is still high accidents caused by many factors, such as driver negligence, vehicle damage, and road damage. However, transportation technology developed very rapidly, for example, a smart car. The smart car is land transportation that does not use humans as drivers but uses machines automatically. However, vehicle accidents are still possible because automatic machines do not have the intelligence like humans to see all the vehicle's obstacles. Obstacles can take many forms, one of them is road potholes. We propose a method for detecting road potholes using the Gray-Level Cooccurrence Matrix with three features and using the Support Vector Machine as a classification method. We analyze the combination of GLCM Contrast, Correlation, and Dissimilarity features. The results showed that the combination of Contrast and Dissimilarity features had the best accuracy of 92.033 %, with a computing time of 0.0704 seconds per frame. |
| first_indexed | 2024-03-07T18:52:56Z |
| format | Article |
| id | doaj.art-0461f2309086496d84bf3ebad7b5e295 |
| institution | Directory Open Access Journal |
| issn | 2338-0403 |
| language | English |
| last_indexed | 2024-03-07T18:52:56Z |
| publishDate | 2021-01-01 |
| publisher | Diponegoro University |
| record_format | Article |
| series | Jurnal Teknologi dan Sistem Komputer |
| spelling | doaj.art-0461f2309086496d84bf3ebad7b5e2952024-03-02T00:57:37ZengDiponegoro UniversityJurnal Teknologi dan Sistem Komputer2338-04032021-01-0191646910.14710/jtsiskom.2020.1382812849Three combination value of extraction features on GLCM for detecting pothole and asphalt roadYoke Kusuma Arbawa0Fitri Utaminingrum1https://orcid.org/0000-0002-0281-9429Eko Setiawan2https://orcid.org/0000-0003-2143-9509Faculty of Computer Science, Brawijaya University. Veteran Road, Malang, Indonesia 65145, IndonesiaFaculty of Computer Science, Brawijaya University. Veteran Road, Malang, Indonesia 65145, IndonesiaFaculty of Computer Science, Brawijaya University. Veteran Road, Malang, Indonesia 65145, IndonesiaThe rate of vehicle accidents in various regions is still high accidents caused by many factors, such as driver negligence, vehicle damage, and road damage. However, transportation technology developed very rapidly, for example, a smart car. The smart car is land transportation that does not use humans as drivers but uses machines automatically. However, vehicle accidents are still possible because automatic machines do not have the intelligence like humans to see all the vehicle's obstacles. Obstacles can take many forms, one of them is road potholes. We propose a method for detecting road potholes using the Gray-Level Cooccurrence Matrix with three features and using the Support Vector Machine as a classification method. We analyze the combination of GLCM Contrast, Correlation, and Dissimilarity features. The results showed that the combination of Contrast and Dissimilarity features had the best accuracy of 92.033 %, with a computing time of 0.0704 seconds per frame.https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13828potholedetectionglcmsvmtransportation |
| spellingShingle | Yoke Kusuma Arbawa Fitri Utaminingrum Eko Setiawan Three combination value of extraction features on GLCM for detecting pothole and asphalt road Jurnal Teknologi dan Sistem Komputer pothole detection glcm svm transportation |
| title | Three combination value of extraction features on GLCM for detecting pothole and asphalt road |
| title_full | Three combination value of extraction features on GLCM for detecting pothole and asphalt road |
| title_fullStr | Three combination value of extraction features on GLCM for detecting pothole and asphalt road |
| title_full_unstemmed | Three combination value of extraction features on GLCM for detecting pothole and asphalt road |
| title_short | Three combination value of extraction features on GLCM for detecting pothole and asphalt road |
| title_sort | three combination value of extraction features on glcm for detecting pothole and asphalt road |
| topic | pothole detection glcm svm transportation |
| url | https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13828 |
| work_keys_str_mv | AT yokekusumaarbawa threecombinationvalueofextractionfeaturesonglcmfordetectingpotholeandasphaltroad AT fitriutaminingrum threecombinationvalueofextractionfeaturesonglcmfordetectingpotholeandasphaltroad AT ekosetiawan threecombinationvalueofextractionfeaturesonglcmfordetectingpotholeandasphaltroad |