Indian pothole detection based on CNN and anchor-based deep learning method

Detecting potholes is one of the important tasks for determining proper strategies in ITS (Intelligent Transportation System) service and road management system. Several efforts have been made for developing a technology which can automatically detect and recognize potholes. The main contribution of...

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Bibliographic Details
Published in:International journal of information technology (Singapore. Online) 2022, Vol.14 (7), p.3343-3353
Main Authors: Anandhalli, Mallikarjun, Tanuja, A., Baligar, Vishwanath P., Baligar, Pavana
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Imaging
Computer Science
Image Processing and Computer Vision
Machine Learning
Original Research
Pattern Recognition and Graphics
Software Engineering
Vision
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Summary:Detecting potholes is one of the important tasks for determining proper strategies in ITS (Intelligent Transportation System) service and road management system. Several efforts have been made for developing a technology which can automatically detect and recognize potholes. The main contribution of this paper lies in collecting the pothole data in different Indian traffic conditions and detecting of the same using a vision-based method by defining the performance of deep learning methods like sequential convolutional neural network (CNN), and anchor-based You only Look Once3 (YOLOV3) and analyzing the models in terms of resources and performance of detection. The experiments were conducted on both models and a conclusion was drawn to bring out the benefits of the model with 98% accuracy using CNN and 83% precision using Yolov3.
ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-022-00881-5