Pothole detection using convolutional neural network

A pothole is the most prevalent sort of road issue that must be assessed before repairs can be carried out. Along with the regular wear and tear of vehicles, it is a major factor in traffic accidents. The collecting and analysis of data on road defects is vital for the evaluation of road concerns. D...

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Bibliographic Details
Main Authors: Srivani, B., Kamala, Ch, Deepti, S. Renu, Aakash, G.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Data collection
Highway maintenance
Image classification
Neural networks
Roads
Traffic accidents
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Summary:A pothole is the most prevalent sort of road issue that must be assessed before repairs can be carried out. Along with the regular wear and tear of vehicles, it is a major factor in traffic accidents. The collecting and analysis of data on road defects is vital for the evaluation of road concerns. Data collection through a variety of imaging modalities has been automated, but fault assessment is still performed by hand. Slowing down the highway maintenance process by manually locating and grading potholes is costly and time-consuming. As explained in this paper, potholes in road images can be detected and classified using convolutional neural networks. In order to classify images into potholes and non-potholes, we used convolutional neural networks and trained models. As opposed to seven pre-trained models, convolutional neural networks were compared to see how well they performed in terms of precision and recall accuracy. Potholes may be accurately detected on road photos by trained models InseptionResNetV2 and DenseNet201 with an accuracy of 89.66 percent, according to the research findings.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0198902