Pothole detection using CNN OpenCV and KERAS backed by tensorflow

Potholes have proven to be a major roadblock in recent times. Potholes are one of the leading causes of car accidents and other untoward incidents on the road, and they also cause vehicle wear and tear. The collected data is currently being processed in order to determine road faults and work on the...

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Bibliographic Details
Main Authors: Hadimani, Basavaraj, Aafreen, Sharma, Abhishek, Singh, Aryan, Supriya
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Automation
Data collection
Evaluation
Road maintenance
Online Access:Get full text
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Summary:Potholes have proven to be a major roadblock in recent times. Potholes are one of the leading causes of car accidents and other untoward incidents on the road, and they also cause vehicle wear and tear. The collected data is currently being processed in order to determine road faults and work on them. Data collection is almost fully automated thanks to a variety of imaging systems, but the evaluation of defects based on the collected data is still performed manually and is not automated even today. This method of classifying and evaluating potholes is expensive, time-consuming, labor-intensive, and repetitive, and inevitably, the total road maintenance procedure is slowed. We have described a new detection technique for potholes in this paper. Convolutional neural networks (CNN) have been used. Using this approach in the proposed system, input images were classified into two categories. Implementation was done using OpenCV library in Python. It was trained on 722 raw images and tested on 198 images, with precision, correctness, and recall metrics among the metrics used to assess the results.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0200493