CPDM: An Efficient Crowdsensing-Based Pothole Detection and Measurement System Design

Zhao, Xiaofeng; Wu, Xiaocan; Sun, Yu-E; Huang, He; Du, Yang; Cao, Zhen

doi:10.1109/ictai.2019.00082

Cited by 6 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A system was proposed that provides real-time alerts to drivers using FastDTW and SVM to detect driving events and road abnormalities [44]. For the detection of potholes on road surfaces, a DTW-based separation algorithm for road vibration signals with higher sensitivity was suggested [45]. SVM, HMM, and residual networks (ResNet) were compared for road pavement and non-pavement classification, while KNN and DTW methods were evaluated for anomaly detection [38].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Ozoglu,

Gökgöz

2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

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

Ozoglu,

Gökgöz

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…An augmented KF was developed using the accelerator and gyroscope in smartphones as sensors (Zhao et al, 2019a). Zhong et al (2019) and Zhao et al (2019b) also used smartphone sensors to detect potholes by directly solving system differential equation, while the model parameters are fit using system responses during the underdamped period after passing over a pothole. Other model-based methods, including sliding mode observer (Imine and M'Sirdi, 2006;Rath et al, 2014), jump diffusion process estimator (Li et al, 2016), and Qparameterization (Doumiati et al, 2017), were proposed to estimate road profile and the geometric shape of irregularity.…”

Section: Introductionmentioning

confidence: 99%