Comparative Study of Pothole Recognition Using CNN and YOLOV5

Comparative Study of Pothole Recognition Using CNN and YOLOV5
Author(s):
Anand Upadhyay, Bhavana Mishra, Aditi Singh, Aradhana Pal
Keywords:
Deep learning, Pothole Detection, CNN YOLO, Image processing, TensorFlow.
Abstract
Methods for recognizing potholes on roadsides goal is to evolve plans for real-time or else offline proof of identity potholes, to support real-time resistor of a vehicle (for driver help or independent driving) or else offline data gathering for road preservation. [1]It causes accidents in very high numbers. Therefore, it is a need to carry out timely inspection and maintenance to avoid the problem for road users. This paper projected a deep learning-based model that can perceive potholes initially using images, which can decrease the probability of a fortune. This model is mostly based on Convolutional Neural Network(CNN) and YOLOV5 [2]. The project can reduce the manpower for the maintenance of the roads. This project will be useful for the government for better road maintenance with less manpower in a small period. The Accuracy for the same with image processing techniques such as CNN and YOLO. using this approach, it was possible to detect potholes with an accuracy of 88% and 95% respectively.
Article Details
Unique Paper ID: 158581 Publication Volume & Issue: Volume 9, Issue 10 Page(s): 244 - 250
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