Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection
Pothole repair is one of the paramount tasks in road maintenance. Effective road surface monitoring is an ongoing challenge to the management agency. The current pothole detection, which is conducted image processing with a manual operation, is labour-intensive and time-consuming. Computer vision of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/23/11229 |
| _version_ | 1797508062545707008 |
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| author | Sung-Sik Park Van-Than Tran Dong-Eun Lee |
| author_facet | Sung-Sik Park Van-Than Tran Dong-Eun Lee |
| author_sort | Sung-Sik Park |
| collection | DOAJ |
| description | Pothole repair is one of the paramount tasks in road maintenance. Effective road surface monitoring is an ongoing challenge to the management agency. The current pothole detection, which is conducted image processing with a manual operation, is labour-intensive and time-consuming. Computer vision offers a mean to automate its visual inspection process using digital imaging, hence, identifying potholes from a series of images. The goal of this study is to apply different YOLO models for pothole detection. Three state-of-the-art object detection frameworks (i.e., YOLOv4, YOLOv4-tiny, and YOLOv5s) are experimented to measure their performance involved in real-time responsiveness and detection accuracy using the image set. The image set is identified by running the deep convolutional neural network (CNN) on several deep learning pothole detectors. After collecting a set of 665 images in 720 × 720 pixels resolution that captures various types of potholes on different road surface conditions, the set is divided into training, testing, and validation subsets. A mean average precision at 50% Intersection-over-Union threshold (mAP_0.5) is used to measure the performance of models. The study result shows that the mAP_0.5 of YOLOv4, YOLOv4-tiny, and YOLOv5s are 77.7%, 78.7%, and 74.8%, respectively. It confirms that the YOLOv4-tiny is the best fit model for pothole detection. |
| first_indexed | 2024-03-10T04:57:01Z |
| format | Article |
| id | doaj.art-35f2b64553274a019f2a315922cb9e4d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:57:01Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-35f2b64553274a019f2a315922cb9e4d2023-11-23T02:04:49ZengMDPI AGApplied Sciences2076-34172021-11-0111231122910.3390/app112311229Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole DetectionSung-Sik Park0Van-Than Tran1Dong-Eun Lee2Department of Civil Engineering, Kyungpook National University, Daegu 41566, KoreaDepartment of Civil Engineering, Kyungpook National University, Daegu 41566, KoreaDepartment of Architectural Engineering, Kyungpook National University, Daegu 41566, KoreaPothole repair is one of the paramount tasks in road maintenance. Effective road surface monitoring is an ongoing challenge to the management agency. The current pothole detection, which is conducted image processing with a manual operation, is labour-intensive and time-consuming. Computer vision offers a mean to automate its visual inspection process using digital imaging, hence, identifying potholes from a series of images. The goal of this study is to apply different YOLO models for pothole detection. Three state-of-the-art object detection frameworks (i.e., YOLOv4, YOLOv4-tiny, and YOLOv5s) are experimented to measure their performance involved in real-time responsiveness and detection accuracy using the image set. The image set is identified by running the deep convolutional neural network (CNN) on several deep learning pothole detectors. After collecting a set of 665 images in 720 × 720 pixels resolution that captures various types of potholes on different road surface conditions, the set is divided into training, testing, and validation subsets. A mean average precision at 50% Intersection-over-Union threshold (mAP_0.5) is used to measure the performance of models. The study result shows that the mAP_0.5 of YOLOv4, YOLOv4-tiny, and YOLOv5s are 77.7%, 78.7%, and 74.8%, respectively. It confirms that the YOLOv4-tiny is the best fit model for pothole detection.https://www.mdpi.com/2076-3417/11/23/11229computer visionreal-timepothole detectiondeep learningYOLO |
| spellingShingle | Sung-Sik Park Van-Than Tran Dong-Eun Lee Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection Applied Sciences computer vision real-time pothole detection deep learning YOLO |
| title | Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection |
| title_full | Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection |
| title_fullStr | Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection |
| title_full_unstemmed | Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection |
| title_short | Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection |
| title_sort | application of various yolo models for computer vision based real time pothole detection |
| topic | computer vision real-time pothole detection deep learning YOLO |
| url | https://www.mdpi.com/2076-3417/11/23/11229 |
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