Design and Development of an Intelligent System for Pothole and Hump Identification on Roads

Shivaleelavathi*, B.G.; Yatnalli, Veeramma; Chinmayi,

doi:10.35940/ijrte.c5936.098319

Cited by 8 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The collaboration of different methods with our novel framework targets a much bigger audience, and since it is very cost effective, it makes this system ideal for road surface monitoring in all regions. [10] 62% Accuracy Ultrasonic Singh et al [11] 59.23% Accuracy Global positioning system receiver and ultrasonic Madli et al [12] 74% Accuracy Ultrasonic and GPS Shivaleelavathi et al [13] 79% Accuracy Raspberry Pi, GPS, ultrasonic Mohamed et al [23] 75.6-87.8% (Accuracy) Accelerometer Astarita et al [18] 90% Accuracy, 35% FP Accelerometer using Smart Phone Proposed system 95.48% (Accuracy) Ultrasonic Sensor, Image Processing and manual Inspection…”

Section: Validation Of Proposed System With Other Techniquesmentioning

confidence: 99%

“…There has been a substantial amount of research done in the past on the detection of potholes [3-5] using various techniques like reconstruction using laser [6], stereo vision [7,8], Light Detection and Raanging (LIDAR) [9], ultrasonic [10][11][12][13], etc. Forrest et al [10] proposed a method of detecting road surface disruptions based on ultrasonic sensors in which the pothole detection rate of 62% was achieved on a paved road.…”

Section: Introductionmentioning

confidence: 99%

“…It was found that the system was 74% efficient and the data could be stored in the cloud. Shivaleelavathi et al [13] also proposed an intelligent system using an ultrasonic sensor based on Raspberry Pi, GPS, and ultrasonic to take corrective measures. Devapriya et al [14] proposed a real-time system that detected speed bumps by the analysis of photos of the road that were further enhanced using a Gaussian Filter (computer vision technique).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Application Study on Road Surface Monitoring Using DTW Based Image Processing and Ultrasonic Sensors

2020

View full text Add to dashboard Cite

Road surface monitoring is an essential problem in providing smooth road infrastructure to commuters. This paper proposed an efficient road surface monitoring using an ultrasonic sensor and image processing technique. A novel cost-effective system, which includes ultrasonic sensors sensing with GPS for the detection of the road surface conditions, was designed and proposed. Dynamic time warping (DTW) technique was incorporated with ultrasonic sensors to improve the classification and accuracy of road surface detecting conditions. A new algorithm, HANUMAN, was proposed for automatic recognition and calculation of pothole and speed bumps. Manual inspection was performed and comparison was undertaken to validate the results. The proposed system showed better efficiency than the previous systems with a 95.50% detection rate for various road surface irregularities. The novel framework will not only identify the road irregularities, but also help in decreasing the number of accidents by alerting drivers.

show abstract

Section: Validation Of Proposed System With Other Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Application Study on Road Surface Monitoring Using DTW Based Image Processing and Ultrasonic Sensors

2020

View full text Add to dashboard Cite

show abstract

“…Many of these accidents are due to potholes [2]. Interestingly, potholes do not only lead to accidents but damage to vehicles and traffic jams safety, repair costs and accident compensation costs [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Estimating cost of pothole repair from digital images using Stereo Vision and Artificial Neural Network

Olaye,

Owraigbo,

Bello

2024

ijamec

View full text Add to dashboard Cite

A significant amount of road maintenance cost goes into pothole repairs. The primary cost factors related to potholes are their size and depth, as larger and thicker potholes incur higher repair costs. However, existing methods for estimating pothole repair in developing countries rely on manual size measurements, which is time consuming, labor intensive, subjective and can lead to poor estimation of repair cost. This paper presents a system that can automatically determine the size of potholes from digital images and estimate the cost of repair. In this study, the stereo vision method was used to automatically estimate the depths of potholes from digital camera images. A feed-forward backward propagation Artificial Neural Network (ANN) was trained using pothole images acquired using mobile phones. The predicted depths and sizes of the potholes were then used to estimate the quantity of materials required to fill the potholes and subsequently, the cumulative cost of repair. Marking out and manual size measurements were performed for twenty randomly selected potholes in the Ugbowo Campus of the University of Benin, Nigeria. These measurements were compared against the estimated sizes of potholes predicted by the ANN model. A system was developed to automatically compute these material costs and considering other cost components such as transportation, labor, and equipment. Results obtained showed that the mean errors for depth, width and height estimates were 3.403%, 3.789% and 5.2617% respectively. Consequently, the developed system correctly estimated the cost of repair of the potholes considered in this study. A significant contribution of the paper is the speed and convenience of acquiring pothole data using a mobile phones without the need for on spot assessment of potholes or use of relatively more expensive stereoscopic camera setup.

show abstract

Pandai Smart Highway

Balakrishnan,

Ooi,

et al. 2023

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Design and Development of an Intelligent System for Pothole and Hump Identification on Roads

Cited by 8 publications

References 3 publications

An Application Study on Road Surface Monitoring Using DTW Based Image Processing and Ultrasonic Sensors

An Application Study on Road Surface Monitoring Using DTW Based Image Processing and Ultrasonic Sensors

Estimating cost of pothole repair from digital images using Stereo Vision and Artificial Neural Network

Pandai Smart Highway

Contact Info

Product

Resources

About