A Review Paper on Pothole Detection Methods

Gandhi, Jetashri R.; Jaliya, U. K.; Thakore, Devendrasingh

doi:10.26438/ijcse/v7i2.379383

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…Pavement characteristics that are generally separately measured include pavement roughness, a vital pavement characteristic measured on a rating index known as the International Roughness Index (IRI) [19]. It is estimated in a moving vehicle from a longitudinal pavement profile with sensors capable of measuring vertical movement [20,21]. Another essential characteristic is transverse deflection, also known as rut depth, measured manually or using sensors that generate transverse pavement profiles [9].…”

Section: Pavement Assessment Indicatorsmentioning

confidence: 99%

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

Qureshi

Hassan

McKeever

et al. 2022

Sensors

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Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems exist, and each uses its own defined subset of pavement characteristics to evaluate pavement conditions. It is noted that automated visual sensing systems using intelligent algorithms can help reduce the cost and time required for assessing the condition of pavement infrastructure, especially for local and regional road networks. However, environmental factors, pavement types, and image collection devices are significant in this domain and lead to challenging variations. Commercial solutions for automatic pavement assessment with certain limitations exist. The topic is also a focus of academic research. More recently, academic research has pivoted toward deep learning, given that image data is now available in some form. However, research to automate pavement distress assessment often focuses on the regional pavement condition assessment standard that a country or state follows. We observe that the criteria a region adopts to make the evaluation depends on factors such as pavement construction type, type of road network in the area, flow and traffic, environmental conditions, and region’s economic situation. We summarized a list of publicly available datasets for distress detection and pavement condition assessment. We listed approaches focusing on crack segmentation and methods concentrating on distress detection and identification using object detection and classification. We segregated the recent academic literature in terms of the camera’s view and the dataset used, the year and country in which the work was published, the F1 score, and the architecture type. It is observed that the literature tends to focus more on distress identification (“presence/absence” detection) but less on distress quantification, which is essential for developing approaches for automated pavement rating.

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Section: Pavement Assessment Indicatorsmentioning

confidence: 99%

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

Qureshi

Hassan

McKeever

et al. 2022

Sensors

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“…Any sensor can fit a different platform if a proper bracket installs to secure the sensor attached to the platform. Sensor use can be any, such as red, green, and blue (RGB) camera, ground-penetrating radar (GPR) (Wolf et al, 2018), infrared (IR) thermography (Gandhi et al, 2019), light detection, and ranging (LIDAR), multispectral (Noi & Kappas, 2018) and hyperspectral camera. Each detection to the defect road can be evaluate based quantitatively.…”

Section: Introductionmentioning

confidence: 99%

Low altitude multispectral mapping for road defect detection

Mukti¹,

Tahar

2021

GEOGRAFIA

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Pothole's defect is major damages indicated the road condition visually, and the structural defects due to some potential causes. Nowadays, new forms of remote sensing technique were widely used, but less studies in the application of low altitude multispectral mapping. The potential of multispectral images is its help better in resolution due to its spectral characteristic. Hence, it helps a lot in feature classification with proper training sample, classifier used, and spectral band composite. Thus, this study aims to extract the defective roads by using the multispectral image of Parrot Sequoia with low flight altitude. This study tries to detect a pothole's existence from band combination and supervised classification other than its common use which ultimately for agriculture purposes. The classifier used in this is Maximum Likelihood, Support vector machine (SVM) and Mahalanobis Distance. 15 different probability of band stacks of green, NIR, red edge, and red band were used as multispectral images. The comparison of the performance between the types of classifier and band combination was modeled and discussed in this study. Classifier algorithm maximum likelihood gives the lowest error of 0.108m² with a combination of NIR + red edge band. SVM gives the lowest error of 0.427m² with a combination of green + NIR + red edge + red band. While Mahalanobis distance gives the lowest error of -0.082m² with a combination of red edge + red band. Averagely, Mahalanobis distance gives the lowest error of 0.299m² of all bands used.

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Development of an Iot Model for Pothole Detection , Counting and Geolocation

OKPE,

Idachaba

2023

Preprint

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A Review Paper on Pothole Detection Methods

Cited by 3 publications

References 10 publications

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment

Low altitude multispectral mapping for road defect detection

Development of an Iot Model for Pothole Detection , Counting and Geolocation

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