Advancing road maintenance with EfficientDet-based pothole monitoring
Effective road maintenance is crucial for ensuring safe and efficient transportation but is often compromised by the widespread occurrence of potholes. This study introduces a novel approach using an EfficientDet-based model for sophisticated pothole monitoring. Potholes pose a significant...
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| Format: | Article |
| Language: | English |
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Faculty of Technical Sciences in Cacak
2025-01-01
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| Series: | Serbian Journal of Electrical Engineering |
| Subjects: | |
| Online Access: | https://doiserbia.nb.rs/img/doi/1451-4869/2025/1451-48692501057J.pdf |
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| author | Jenefa Archpaul Taurshia Antony Kuriakose Bessy Mani Kumar Edward Naveen Vijaya Lincy Archpaul |
| author_facet | Jenefa Archpaul Taurshia Antony Kuriakose Bessy Mani Kumar Edward Naveen Vijaya Lincy Archpaul |
| author_sort | Jenefa Archpaul |
| collection | DOAJ |
| description | Effective road maintenance is crucial for ensuring safe and efficient
transportation but is often compromised by the widespread occurrence of
potholes. This study introduces a novel approach using an EfficientDet-based
model for sophisticated pothole monitoring. Potholes pose a significant
hazard that requires proactive detection and timely resolution. Traditional
detection methods frequently fall short in terms of accuracy and real-time
capability. Addressing these limitations, our research leverages the
EfficientDet architecture, known for its optimal balance of accuracy and
computational efficiency, to enhance the detection and monitoring of
potholes. We utilized a carefully curated dataset from Kaggle, which
includes 1,500 training images, 1,000 validation images, and 500 test
images, encompassing a variety of real-world pothole scenarios. This
diversity enables the model to generalize effectively across different
conditions. Our experimental evaluations demonstrate that the
EfficientDet-based model achieves an impressive average precision of 0.90
and a robust recall of 0.92, highlighting its capacity for accurate and
swift pothole detection-an essential component for improving road
maintenance. Moreover, we provide a comparative analysis with five
contemporary pothole detection algorithms: YOLOv5, RetinaNet, CenterNet,
SSD, and Faster R-CNN, among which EfficientDet consistently shows superior
performance in terms of precision, recall, F1-Score, and average precision.
These findings highlight the significant advancements in road safety,
infrastructure management, and resource optimization. By adopting
sophisticated deep learning techniques like EfficientDet, we promote a
transformative improvement in road maintenance practices, paving the way for
more resilient, safe, and disruptionminimized transportation networks. |
| format | Article |
| id | doaj-art-ddf278cbe36d406c88a3d47f6fe4d440 |
| institution | OA Journals |
| issn | 1451-4869 2217-7183 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Faculty of Technical Sciences in Cacak |
| record_format | Article |
| series | Serbian Journal of Electrical Engineering |
| spelling | doaj-art-ddf278cbe36d406c88a3d47f6fe4d4402025-08-20T02:19:34ZengFaculty of Technical Sciences in CacakSerbian Journal of Electrical Engineering1451-48692217-71832025-01-01221577410.2298/SJEE2501057J1451-48692501057JAdvancing road maintenance with EfficientDet-based pothole monitoringJenefa Archpaul0https://orcid.org/0000-0002-6697-1788Taurshia Antony1https://orcid.org/0000-0001-9129-1859Kuriakose Bessy Mani2https://orcid.org/0009-0008-7952-7927Kumar Edward Naveen Vijaya3https://orcid.org/0009-0003-5845-2173Lincy Archpaul4https://orcid.org/0000-0002-4104-6529Karunya Institute of Technology and Sciences, School of Computer Science and Technology, Coimbatore, IndiaKarunya Institute of Technology and Sciences, School of Computer Science and Technology, Coimbatore, IndiaComputer Science and Engineering, Malla Reddy Engineering College for Women, IndiaComputer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, IndiaDepartment of Computer Science and Engineering, National Engineering College, Kovilpatti, Tamil Nadu, IndiaEffective road maintenance is crucial for ensuring safe and efficient transportation but is often compromised by the widespread occurrence of potholes. This study introduces a novel approach using an EfficientDet-based model for sophisticated pothole monitoring. Potholes pose a significant hazard that requires proactive detection and timely resolution. Traditional detection methods frequently fall short in terms of accuracy and real-time capability. Addressing these limitations, our research leverages the EfficientDet architecture, known for its optimal balance of accuracy and computational efficiency, to enhance the detection and monitoring of potholes. We utilized a carefully curated dataset from Kaggle, which includes 1,500 training images, 1,000 validation images, and 500 test images, encompassing a variety of real-world pothole scenarios. This diversity enables the model to generalize effectively across different conditions. Our experimental evaluations demonstrate that the EfficientDet-based model achieves an impressive average precision of 0.90 and a robust recall of 0.92, highlighting its capacity for accurate and swift pothole detection-an essential component for improving road maintenance. Moreover, we provide a comparative analysis with five contemporary pothole detection algorithms: YOLOv5, RetinaNet, CenterNet, SSD, and Faster R-CNN, among which EfficientDet consistently shows superior performance in terms of precision, recall, F1-Score, and average precision. These findings highlight the significant advancements in road safety, infrastructure management, and resource optimization. By adopting sophisticated deep learning techniques like EfficientDet, we promote a transformative improvement in road maintenance practices, paving the way for more resilient, safe, and disruptionminimized transportation networks.https://doiserbia.nb.rs/img/doi/1451-4869/2025/1451-48692501057J.pdfroad maintenancepothole detectionefficientdettransportation infrastructurereal-time monitoring |
| spellingShingle | Jenefa Archpaul Taurshia Antony Kuriakose Bessy Mani Kumar Edward Naveen Vijaya Lincy Archpaul Advancing road maintenance with EfficientDet-based pothole monitoring Serbian Journal of Electrical Engineering road maintenance pothole detection efficientdet transportation infrastructure real-time monitoring |
| title | Advancing road maintenance with EfficientDet-based pothole monitoring |
| title_full | Advancing road maintenance with EfficientDet-based pothole monitoring |
| title_fullStr | Advancing road maintenance with EfficientDet-based pothole monitoring |
| title_full_unstemmed | Advancing road maintenance with EfficientDet-based pothole monitoring |
| title_short | Advancing road maintenance with EfficientDet-based pothole monitoring |
| title_sort | advancing road maintenance with efficientdet based pothole monitoring |
| topic | road maintenance pothole detection efficientdet transportation infrastructure real-time monitoring |
| url | https://doiserbia.nb.rs/img/doi/1451-4869/2025/1451-48692501057J.pdf |
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