Road Peculiarities Detection using Deep Learning for Vehicle Vision System

Rani, M. Roja; Mustafar, Mushairry; Ismail, Nazrul; Mansor, M.S.F.; Zainuddin, Zainal Abidin

doi:10.1088/1757-899x/1068/1/012001

Cited by 6 publications

(2 citation statements)

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“…M.R. Rani et al [13] proposed a new system for identifying potholes and road bumps in vehicles in addition to ADAS, where the ADAS system can detects vehicles, pedestrians, road lanes and signages only. Here, the pothole and road bumps detection model are built using SSD (Single Stage Detection).…”

Section: Introductionmentioning

confidence: 99%

Pothole detection and dimension estimation by deep learning

Ch,

Tallam

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Maintenance of roads is a crucial part after the construction of roads in order to improve its design life. Without proper maintenance, deterioration occurs more rapidly out of which potholes are the most common type of road distress that can pose a significant hazard to passengers and vehicles. In order to improve road maintenance, automated systems contribute to improving road safety and reducing infrastructure costs. In this paper one such automated pothole detection system is used by applying CNN (Convolution Neural Network) a deep learning approach with the object detection YOLO (You Only Look Once) to detect potholes in real time. The proposed model used here is trained from scratch on a large pothole dataset with an epochs value of 200, and is validated and tested on custom made dataset. The trained model provided accurate results with an mAP50 of 92% in detection of potholes. Further, an image processing method based on spatial resolution factor is used for dimension estimation of the potholes. The findings of this study assist in the inspection of non-destructive automatic pavement conditions that also contributes in improving road safety and reducing the time and cost required for road maintenance.

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Section: Introductionmentioning

confidence: 99%

Pothole detection and dimension estimation by deep learning

Ch,

Tallam

2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…e proposed method achieved high precision 95.2% and recall 92.0%. Rani et al [29] detected potholes and road bumps for an advance driver assistance system (ADAS) using SSD-MobileNet for detection which is trained on their self-made dataset collected from Malaysian roads.…”

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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