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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 1451-4869 2217-7183 |