Study Of Types of Road Abnormalities and Techniques Used for Their Detection

Bhamare, Liladhar; Mitra, Nikita; Varade, Gauri; Mehta, Hrishikesh

doi:10.1109/iceeie52663.2021.9616755

Cited by 8 publications

(2 citation statements)

References 37 publications

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“…Using an Internet of Things (IoT) sensor, various machine learning models (logistic regression (LR), SVM, k-nearest neighbors (KNN), naive Bayes (NB), decision tree (DT), random forest (RF), and ensemble voting) were compared in terms of their performance under different parameters, and it was determined that random forest was the best model for pothole detection [42]. The use of ML techniques and an IoT platform was recommended for the detection of speed bumps, humps, and potholes [43].…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Road Potholes by Applying Convolutional Neural Network Method Based on Road Vibration Data

Ozoglu,

Gökgöz

2023

Sensors

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In the context of road transportation, detecting road surface irregularities, particularly potholes, is of paramount importance due to their implications for driving comfort, transportation costs, and potential accidents. This study presents the development of a system for pothole detection using vibration sensors and the Global Positioning System (GPS) integrated within smartphones, without the need for additional onboard devices in vehicles incurring extra costs. In the realm of vibration-based road anomaly detection, a novel approach employing convolutional neural networks (CNNs) is introduced, breaking new ground in this field. An iOS-based application was designed for the acquisition and transmission of road vibration data using the built-in three-axis accelerometer and gyroscope of smartphones. Analog road data were transformed into pixel-based visuals, and various CNN models with different layer configurations were developed. The CNN models achieved a commendable accuracy rate of 93.24% and a low loss value of 0.2948 during validation, demonstrating their effectiveness in pothole detection. To evaluate the performance further, a two-stage validation process was conducted. In the first stage, the potholes along predefined routes were classified based on the labeled results generated by the CNN model. In the second stage, observations and detections during the field study were used to identify road potholes along the same routes. Supported by the field study results, the proposed method successfully detected road potholes with an accuracy ranging from 80% to 87%, depending on the specific route.

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Section: Literature Reviewmentioning

confidence: 99%

Detection of Road Potholes by Applying Convolutional Neural Network Method Based on Road Vibration Data

Ozoglu,

Gökgöz

2023

Sensors

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“…ARDAD systems can significantly ease the day-to-day maintenance process and reduce the loss of life and costs associated with traffic-related injuries [ 3 ]. However, despite the growing number of publications on ARDAD systems since 2020, most surveys focus on one or two of many problem domains, such as (a) road surface cracks [ 8 , 9 , 10 ], (b) road surface defects [ 10 , 11 , 12 ], (c) structural damage [ 13 , 14 ], or (d) anomaly detection [ 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Road Defect and Anomaly Detection for Traffic Safety: A Systematic Review

Rathee

Bačić

Doborjeh

2023

Sensors

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Recently, there has been a substantial increase in the development of sensor technology. As enabling factors, computer vision (CV) combined with sensor technology have made progress in applications intended to mitigate high rates of fatalities and the costs of traffic-related injuries. Although past surveys and applications of CV have focused on subareas of road hazards, there is yet to be one comprehensive and evidence-based systematic review that investigates CV applications for Automated Road Defect and Anomaly Detection (ARDAD). To present ARDAD’s state-of-the-art, this systematic review is focused on determining the research gaps, challenges, and future implications from selected papers (N = 116) between 2000 and 2023, relying primarily on Scopus and Litmaps services. The survey presents a selection of artefacts, including the most popular open-access datasets (D = 18), research and technology trends that with reported performance can help accelerate the application of rapidly advancing sensor technology in ARDAD and CV. The produced survey artefacts can assist the scientific community in further improving traffic conditions and safety.

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