Road Surface Classification and Subsequent Pothole Detection Using Deep Learning

Agrawal, Ravi; Chhadva, Yash; Addagarla, Sanjana; Chaudhari, Shilpa

doi:10.1109/incet51464.2021.9456126

Cited by 21 publications

(4 citation statements)

References 12 publications

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“…Many approaches rely solely on computer vision techniques for pothole detection [6], [7], [9] - [19]. The inherent problem with using only computer vision-based approaches is that they are highly weather-dependent, and physical factors like fog or rain greatly affect the mapping process.…”

Section: Research Gapmentioning

confidence: 99%

A Pothole Can Be Seen with Two Eyes: An Ensemble Approach to Pothole Detection

Patawar,

Mehdi,

Kore

et al. 2024

Preprint

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The issue of potholes and bad road quality is faced by nearly every developing country. These conditions often lead to accidents and traffic and cause the general public inconvenience. Significant research work has been performed in the field of effective pothole and road quality detection but due to several underlying issues like relying on only computer vision or sensor-based modeling, or requiring specialized equipment like LiDAR, implementing these solutions in the real world has never been feasible, especially for developing countries where a reliable system is required which works under any condition and more importantly, can be implemented with minimal resources and changes to the existing infrastructure. The proposed system is an end-to-end robust architecture that utilizes mobile devices mounted on government vehicles for data collection and features an innovative ensemble of a YOLOS and a YOLOv8 model resulting in a 97.34% mAP@0.50 score for pothole detection, in conjunction with state-of-the-art sensor based pothole detection and road quality detection models resulting in impressive accuracies of 98.5% and 95.4% respectively. This two-forked approach to pothole and road quality detection combined with innovative connected applications like an analytics dashboard for the government and a navigation application for the consumer make this entire system highly relevant while being scalable, reliable, and easily deployable using the existing infrastructure in developing countries.

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Section: Research Gapmentioning

confidence: 99%

A Pothole Can Be Seen with Two Eyes: An Ensemble Approach to Pothole Detection

Patawar,

Mehdi,

Kore

et al. 2024

Preprint

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show abstract

“…The approach shows potential for a possible adoption and deployment for use in developing nations. Furthermore, in [21], a CNN-based model, especially YOLOV3, was proposed for the classification of various road types, including paved, unpaved, and asphalt roads. In addition, the suggested model was trained to detect the existence of potholes on classed asphalt images with an accuracy of 96%, while the road classification achieved an accuracy of 88%.…”

Section: Deep Learning Approaches For Pothole Detectionmentioning

confidence: 99%

A Critical Appraisal of Various Implementation Approaches for Real-time Pothole Anomaly Detection: Toward Safer Roads in Developing Nations

Bello-Salau,

Onumanyi,

Adebiyi

et al. 2023

The 4th International Electronic Conference on Applied Sciences

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“…K-means and Sobel edge detection algorithms did not detect and localize potholes accurately as CNN did. In [24], three different datasets are used to classify three different road types such as paved, unpaved, and asphalt for the further classification of the pothole in each road type. Finally, the CNN model uses 7,000 images of datasets that are RTK, KITTI, and caRINA for training.…”

Section: Introductionmentioning

confidence: 99%

Pothole Detection Using Deep Learning: A Real‐Time and AI‐on‐the‐Edge Perspective

Asad¹,

Khaliq²,

Yousaf

et al. 2022

Advances in Civil Engineering

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Asphalt pavement distresses are the major concern of underdeveloped and developed nations for the smooth running of daily life commute. Among various pavement failures, numerous research can be found on pothole detection as they are injurious to automobiles and passengers that may turn into an accident. This work is intended to explore the potential of deep learning models and deploy three superlative deep learning models on edge devices for pothole detection. In this work, we have exploited the AI kit (OAK-D) on a single-board computer (Raspberry Pi) as an edge platform for pothole detection. Detailed real-time performance comparison of state-of-the-art deep learning models and object detection frameworks (YOLOv1, YOLOv2, YOLOv3, YOLOv4, Tiny-YOLOv4, YOLOv5, and SSD-mobilenetv2) for pothole detection is presented. The experimentation is performed on an image dataset with pothole in diverse road conditions and illumination variations as well as on real-time video captured through a moving vehicle. The Tiny-YOLOv4, YOLOv4, and YOLOv5 evince the highest mean average precision (mAP) of 80.04%, 85.48%, and 95%, respectively, on the image set, thus proving the strength of the proposed approach for pothole detection and deployed on OAK-D for real-time detection. The study corroborated Tiny-YOLOv4 as the befitted model for real-time pothole detection with 90% detection accuracy and 31.76 FPS.

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Road Surface Classification and Subsequent Pothole Detection Using Deep Learning

Cited by 21 publications

References 12 publications

A Pothole Can Be Seen with Two Eyes: An Ensemble Approach to Pothole Detection

A Pothole Can Be Seen with Two Eyes: An Ensemble Approach to Pothole Detection

A Critical Appraisal of Various Implementation Approaches for Real-time Pothole Anomaly Detection: Toward Safer Roads in Developing Nations

Pothole Detection Using Deep Learning: A Real‐Time and AI‐on‐the‐Edge Perspective

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