Pothole detection using UAV with deep learning algorithm for road inspection
Pothole detection is a crucial component of road maintenance, essential for ensuring safety and minimizing vehicle damage. Traditional road inspection methods are often limited by their coverage, labor-intensive nature, and time-consuming processes. This paper presents an innovative approach to poth...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/118807/1/118807.pdf https://ir.uitm.edu.my/id/eprint/118807/ |
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| Summary: | Pothole detection is a crucial component of road maintenance, essential for ensuring safety and minimizing vehicle damage. Traditional road inspection methods are often limited by their coverage, labor-intensive nature, and time-consuming processes. This paper presents an innovative approach to pothole detection by utilizing Unmanned Aerial Vehicles (UAVs) in combination with a Convolutional Neural Network (CNN) algorithm. The primary aim of this study is to evaluate the effectiveness of the CNN algorithm in detecting road potholes. The results indicate a high level of detection confidence, demonstrating that UAVs operating at low altitudes can accurately capture orthophotos for pothole identification. The pothole detection model achieved a precision of 0.437, a recall of 0.800, and a mean average precision (mAP) of 0.740, highlighting its accuracy and reliability. The study concludes that while UAVs integrated with artificial intelligence show promise for effective pothole detection, low-altitude flights present practical challenges due to environmental factors. Despite these limitations, the combination of UAVs and CNNs offers a viable solution for enhancing road inspection efficiency and accuracy. |
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