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Smart Pothole Detection System using Deep Learning Algorithms

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Abstract

Potholes are a threat on roads, and their presence compromises driver, vehicle, and pedestrian safety. In developing countries, the primary reason for road accidents is bad road conditions, resulting in human life and property loss. In countries like India, Road maintenance is a challenging activity. Accidents rates are increasing year by year due to the up-surging potholes count. This paper presents the system as "Forward View Guidance for pothole detection for Indian passenger Car." The camera captures video images, and a deep learning algorithm is used to classify the images as potholes and regular roads. The camera will also provide a view of the vehicle's front, highlighting the pothole. Deep learning YOLOv3 and YOLOv5 algorithms are used to train the model and tested with the Kaggle pothole detection datasets to predict the model's accuracy for detection. The proposed system will help monitor the road's condition, count the number of potholes on the road, and generate an alert signal. The performance of the proposed system is evaluated using precision, recall, and average precision (AP). The experimentation results show that the YOLOv5 algorithm performs best than the YOLOv3 algorithm. The precision, recall, and average precision (AP) for YOLOv5 are obtained as 0.763, 0.548, and 0.635, respectively. The system algorithm is implemented on the Raspberry Pi 4B model, which can be easily fitted as an addon system in the vehicle.

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Abbreviations

AI:

Artificial Intelligence

ADAS:

Advanced Driver Assistance System

ML:

Machine Learning

IoT:

Internet of Things

SVM:

Support Vector Machine

KNN:

K Nearest Neighbor

CNN:

Convolutional Neural Network

R-CNN:

Region-based Convolutional Neural Network.

YOLO:

You Only Look Once

References

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Contributions

SC carried out literature review, contributed in data collection, analysis and interpretation of results, and was major contributor in manuscript preparation. AB helped in hardware implementation and final drafting of manuscript. All authors read and approved the final manuscript.

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Correspondence to Savita Chougule.

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Chougule, S., Barhatte, A. Smart Pothole Detection System using Deep Learning Algorithms. Int. J. ITS Res. 21, 483–492 (2023). https://doi.org/10.1007/s13177-023-00363-3

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  • DOI: https://doi.org/10.1007/s13177-023-00363-3

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