YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images
Roads are crucial infrastructure for connectivity and transportation. Various asphalt pavement distresses pose safety risks to road users and vehicles. Early detection of these distresses is crucial for road safety and efficiency. This study proposes employing the deep learning algorithm YOLOv9, the...
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| Format: | Article |
| Language: | English |
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Universitas Riau
2025-06-01
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| Series: | International Journal of Electrical, Energy and Power System Engineering |
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| Online Access: | https://ijeepse.id/journal/index.php/ijeepse/article/view/239 |
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| author | Fahrizal Siti Nurjanah Yoan Purbolingga Dila Marta Putri Asde Rahmawati Bastul Wajhi Akramunnas Muhidin Arifin |
| author_facet | Fahrizal Siti Nurjanah Yoan Purbolingga Dila Marta Putri Asde Rahmawati Bastul Wajhi Akramunnas Muhidin Arifin |
| author_sort | Fahrizal |
| collection | DOAJ |
| description | Roads are crucial infrastructure for connectivity and transportation. Various asphalt pavement distresses pose safety risks to road users and vehicles. Early detection of these distresses is crucial for road safety and efficiency. This study proposes employing the deep learning algorithm YOLOv9, the latest You Only Look Once (YOLO) version, for identifying asphalt pavement distresses. YOLOv9 excels in efficiency, accuracy, and computational speed compared to its predecessors. The trained model exhibited outstanding performance in identifying different types of pavement distresses, especially potholes, rutting, and alligator cracking, achieving a precision of 1.0 (at a confidence threshold of 0.969), a recall of 0.9 (at a threshold of 0.0), and a mAP of 0.796. It also demonstrated swift image processing at 2.7 ms per image. While the model struggles with accurately identifying longitudinal cracking at higher confidence levels, its effectiveness in visualizing distress detection results on different pavement types is commendable. This YOLOv9-based system has promising potential for integration into road maintenance and monitoring protocols, enabling prompt identification and rectification of road issues to enhance safety and convenience for road users. Further enhancements are necessary to refine longitudinal cracking detection and evaluate the model across a broader spectrum of road conditions. |
| format | Article |
| id | doaj-art-ed24e284cc804534ad9ef16d65adb5f8 |
| institution | Matheson Library |
| issn | 2654-4644 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Universitas Riau |
| record_format | Article |
| series | International Journal of Electrical, Energy and Power System Engineering |
| spelling | doaj-art-ed24e284cc804534ad9ef16d65adb5f82025-07-07T15:15:41ZengUniversitas RiauInternational Journal of Electrical, Energy and Power System Engineering2654-46442025-06-018220821910.31258/ijeepse.8.2.208-219239YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway ImagesFahrizal0Siti Nurjanah1Yoan Purbolingga2Dila Marta Putri3Asde Rahmawati4Bastul Wajhi Akramunnas5Muhidin Arifin6Institut Teknologi Bisnis Riau, IndonesiaUniversitas Riau, IndonesiaUniversitas Andalas, Padang, IndonesiaInstitut Teknologi Bisnis Riau, IndonesiaInstitut Teknologi Bisnis Riau, IndonesiaInstitut Teknologi Bisnis Riau, IndonesiaUniversity of Selangor, MalaysiaRoads are crucial infrastructure for connectivity and transportation. Various asphalt pavement distresses pose safety risks to road users and vehicles. Early detection of these distresses is crucial for road safety and efficiency. This study proposes employing the deep learning algorithm YOLOv9, the latest You Only Look Once (YOLO) version, for identifying asphalt pavement distresses. YOLOv9 excels in efficiency, accuracy, and computational speed compared to its predecessors. The trained model exhibited outstanding performance in identifying different types of pavement distresses, especially potholes, rutting, and alligator cracking, achieving a precision of 1.0 (at a confidence threshold of 0.969), a recall of 0.9 (at a threshold of 0.0), and a mAP of 0.796. It also demonstrated swift image processing at 2.7 ms per image. While the model struggles with accurately identifying longitudinal cracking at higher confidence levels, its effectiveness in visualizing distress detection results on different pavement types is commendable. This YOLOv9-based system has promising potential for integration into road maintenance and monitoring protocols, enabling prompt identification and rectification of road issues to enhance safety and convenience for road users. Further enhancements are necessary to refine longitudinal cracking detection and evaluate the model across a broader spectrum of road conditions.https://ijeepse.id/journal/index.php/ijeepse/article/view/239asphalt pavement distressesdeep learningobject detectionroad safetyyolov9 |
| spellingShingle | Fahrizal Siti Nurjanah Yoan Purbolingga Dila Marta Putri Asde Rahmawati Bastul Wajhi Akramunnas Muhidin Arifin YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images International Journal of Electrical, Energy and Power System Engineering asphalt pavement distresses deep learning object detection road safety yolov9 |
| title | YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images |
| title_full | YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images |
| title_fullStr | YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images |
| title_full_unstemmed | YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images |
| title_short | YOLOv9: A High-Performance Deep Learning Approach for Asphalt Pavement Distresses Detection in Roadway Images |
| title_sort | yolov9 a high performance deep learning approach for asphalt pavement distresses detection in roadway images |
| topic | asphalt pavement distresses deep learning object detection road safety yolov9 |
| url | https://ijeepse.id/journal/index.php/ijeepse/article/view/239 |
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