Road Damage Detection Using Yolov9-Based Imagery
Road damage is one of the leading factors contributing to traffic accidents. Rapid identification and repair of damaged roads are crucial in road infrastructure management. This study aims to develop an effective method for detecting road damage, utilizing the YOLOv9 algorithm as a key component, su...
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| Format: | Article |
| Language: | English |
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LPPM ISB Atma Luhur
2025-05-01
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| Series: | Jurnal Sisfokom |
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| Online Access: | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2377 |
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| author | Febrian Akbar Azhari Tatang Rohana Kiki Ahmad Baihaqi Ahmad Fauzi |
| author_facet | Febrian Akbar Azhari Tatang Rohana Kiki Ahmad Baihaqi Ahmad Fauzi |
| author_sort | Febrian Akbar Azhari |
| collection | DOAJ |
| description | Road damage is one of the leading factors contributing to traffic accidents. Rapid identification and repair of damaged roads are crucial in road infrastructure management. This study aims to develop an effective method for detecting road damage, utilizing the YOLOv9 algorithm as a key component, such as cracks and potholes, using the Convolutional Neural Network (CNN) approach. YOLOv9 was chosen due to its efficient architecture, which enables real-time object detection, and its proven effectiveness in various object detection tasks. An annotated dataset of road images was used during the model training and testing process. The results show that the YOLOv9 model can accurately detect road damage. The model achieved a precision of 0.85 and a recall of 0.992 for pothole detection, and a precision of 0.94 for crack detection. Evaluation using mAP50 yielded a score of 0.96, while mAP50-95 reached 0.77, indicating strong detection and classification capability. A consistent decline in loss functions during training also signifies effective learning by the model. These findings suggest that YOLOv9 has the potential to be implemented in automated road damage detection systems, which can accelerate maintenance processes and enhance road user safety. |
| format | Article |
| id | doaj-art-eb0e42bbb9f2479d8f8bbe2f702035b5 |
| institution | Kabale University |
| issn | 2301-7988 2581-0588 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | LPPM ISB Atma Luhur |
| record_format | Article |
| series | Jurnal Sisfokom |
| spelling | doaj-art-eb0e42bbb9f2479d8f8bbe2f702035b52025-08-20T03:33:02ZengLPPM ISB Atma LuhurJurnal Sisfokom2301-79882581-05882025-05-0114219620110.32736/sisfokom.v14i2.23772040Road Damage Detection Using Yolov9-Based ImageryFebrian Akbar Azhari0Tatang Rohana1Kiki Ahmad Baihaqi2Ahmad Fauzi3Department of Computer Science, University of Buana Perjuangan Karawang Department of Computer Science, University of Buana Perjuangan Karawang Department of Computer Science, University of Buana Perjuangan Karawang Department of Computer Science, University of Buana Perjuangan Karawang `Road damage is one of the leading factors contributing to traffic accidents. Rapid identification and repair of damaged roads are crucial in road infrastructure management. This study aims to develop an effective method for detecting road damage, utilizing the YOLOv9 algorithm as a key component, such as cracks and potholes, using the Convolutional Neural Network (CNN) approach. YOLOv9 was chosen due to its efficient architecture, which enables real-time object detection, and its proven effectiveness in various object detection tasks. An annotated dataset of road images was used during the model training and testing process. The results show that the YOLOv9 model can accurately detect road damage. The model achieved a precision of 0.85 and a recall of 0.992 for pothole detection, and a precision of 0.94 for crack detection. Evaluation using mAP50 yielded a score of 0.96, while mAP50-95 reached 0.77, indicating strong detection and classification capability. A consistent decline in loss functions during training also signifies effective learning by the model. These findings suggest that YOLOv9 has the potential to be implemented in automated road damage detection systems, which can accelerate maintenance processes and enhance road user safety.https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2377yolov9road damage detectioncnndeep learningmachine learning |
| spellingShingle | Febrian Akbar Azhari Tatang Rohana Kiki Ahmad Baihaqi Ahmad Fauzi Road Damage Detection Using Yolov9-Based Imagery Jurnal Sisfokom yolov9 road damage detection cnn deep learning machine learning |
| title | Road Damage Detection Using Yolov9-Based Imagery |
| title_full | Road Damage Detection Using Yolov9-Based Imagery |
| title_fullStr | Road Damage Detection Using Yolov9-Based Imagery |
| title_full_unstemmed | Road Damage Detection Using Yolov9-Based Imagery |
| title_short | Road Damage Detection Using Yolov9-Based Imagery |
| title_sort | road damage detection using yolov9 based imagery |
| topic | yolov9 road damage detection cnn deep learning machine learning |
| url | https://jurnal.atmaluhur.ac.id/index.php/sisfokom/article/view/2377 |
| work_keys_str_mv | AT febrianakbarazhari roaddamagedetectionusingyolov9basedimagery AT tatangrohana roaddamagedetectionusingyolov9basedimagery AT kikiahmadbaihaqi roaddamagedetectionusingyolov9basedimagery AT ahmadfauzi roaddamagedetectionusingyolov9basedimagery |