Watch out, pothole! featuring road damage detection in an end-to-end system for autonomous driving

Kortmann, Felix; Funk, Burkhardt; Drews, Paul

doi:10.1016/j.datak.2022.102091

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…In contrast, applying a risk assessment requires using 2D pothole images that can predict dangerous potholes before a vehicle passes through the pothole. Kortmann et al [35] performed severity classification using deep learning based on 2D road images to establish a route plan for an autonomous vehicle. The author classified road hazards into three stages: low, medium, and high, depending on the road features, potholes, and depths of the road surface.…”

Section: Risk Assessment Methodsmentioning

confidence: 99%

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Image-Based Pothole Detection Using Multi-Scale Feature Network and Risk Assessment

Heo

Choi

Kim³

et al. 2023

Electronics

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Potholes on road surfaces pose a serious hazard to vehicles and passengers due to the difficulty detecting them and the short response time. Therefore, many government agencies are applying various pothole-detection algorithms for road maintenance. However, current methods based on object detection are unclear in terms of real-time detection when using low-spec hardware systems. In this study, the SPFPN-YOLOv4 tiny was developed by combining spatial pyramid pooling and feature pyramid network with CSPDarknet53-tiny. A total of 2665 datasets were obtained via data augmentation, such as gamma regulation, horizontal flip, and scaling to compensate for the lack of data, and were divided into training, validation, and test of 70%, 20%, and 10% ratios, respectively. As a result of the comparison of YOLOv2, YOLOv3, YOLOv4 tiny, and SPFPN-YOLOv4 tiny, the SPFPN-YOLOv4 tiny showed approximately 2–5% performance improvement in the mean average precision (intersection over union = 0.5). In addition, the risk assessment based on the proposed SPFPN-YOLOv4 tiny was calculated by comparing the tire contact patch size with pothole size by applying the pinhole camera and distance estimation equation. In conclusion, we developed an end-to-end algorithm that can detect potholes and classify the risks in real-time using 2D pothole images.

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Section: Risk Assessment Methodsmentioning

confidence: 99%

“…The lower the IoU value, the farthest distance between the centroids and the point is calculated; therefore, it is possible to set the center point farthest from the point. Anchor box values applied with K-means ++ clustering are [35,21] [40,22]. By setting the number of anchor boxes to 9, an IoU score of 71.02% was achieved.…”

Section: K-means++ Clusteringmentioning

confidence: 99%

Image-Based Pothole Detection Using Multi-Scale Feature Network and Risk Assessment

Heo

Choi

Kim³

et al. 2023

Electronics

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“…Hybrid methods include a combination of multiple ML or DL methods used in CV techniques. There are many intelligent transportation applications for this approach, such as license plate recognition [ 85 , 100 , 101 ], video anomaly detection [ 68 , 89 , 92 , 102 ], automatic license plate recognition [ 25 , 103 ], vehicle detection [ 11 , 12 , 53 , 55 ], pedestrian detection [ 58 , 104 ], lane line detection [ 63 , 105 ], obstacle detection [ 106 , 107 , 108 , 109 , 110 ], structural damage detection [ 111 , 112 , 113 ], and autonomous vehicle applications [ 13 , 114 , 115 ].…”

Section: Computer Vision Studies In the Field Of Itsmentioning

confidence: 99%

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Dilek

Dener

2023

Sensors

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As technology continues to develop, computer vision (CV) applications are becoming increasingly widespread in the intelligent transportation systems (ITS) context. These applications are developed to improve the efficiency of transportation systems, increase their level of intelligence, and enhance traffic safety. Advances in CV play an important role in solving problems in the fields of traffic monitoring and control, incident detection and management, road usage pricing, and road condition monitoring, among many others, by providing more effective methods. This survey examines CV applications in the literature, the machine learning and deep learning methods used in ITS applications, the applicability of computer vision applications in ITS contexts, the advantages these technologies offer and the difficulties they present, and future research areas and trends, with the goal of increasing the effectiveness, efficiency, and safety level of ITS. The present review, which brings together research from various sources, aims to show how computer vision techniques can help transportation systems to become smarter by presenting a holistic picture of the literature on different CV applications in the ITS context.

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Enhancing Nighttime Vehicle Segmentation for Autonomous Driving Based on YOLOv5

Huang

2024

Communications in Computer and Information Science

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Watch out, pothole! featuring road damage detection in an end-to-end system for autonomous driving

Cited by 8 publications

References 27 publications

Image-Based Pothole Detection Using Multi-Scale Feature Network and Risk Assessment

Image-Based Pothole Detection Using Multi-Scale Feature Network and Risk Assessment

Computer Vision Applications in Intelligent Transportation Systems: A Survey

Enhancing Nighttime Vehicle Segmentation for Autonomous Driving Based on YOLOv5

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