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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mohamed Anis Benallal
Universite des Sciences et de la Technologie d’Oran Mohamed Boudiaf (USTO-MB)
Computer Science & IT Engineering

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An image-based convolutional neural network system for road defects detection Mohamed Anis Benallal; Mustapha Si Tayeb
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i2.pp577-584

Abstract

An application of convolutional neural network (CNN) technique for road surface defects detection is presented in this paper. You only look ones (YOLO) algorithm showed its capabilities as an effective object detection technique in many previous works for different problems. Road damages detection and classification is one of the most challenging problems faced by public and private road management agencies. We present here results for a first attempt on applying YOLO to detect cracks and potholes, the most common defects encountered in surface roadways. Image database of the Brazilian highways were used to prepare input data, train the model and test it. Despite considering different types of cracks in one class and a less amount of potholes images, results show that the YOLO algorithm performs well with a global rate of 91% of defect detection. Output results analysis induce us to work on providing a local database for Algerian roadways with a large number of defect images/videos, as well as producing an automatic road-dedicated defects detector device.
Co-Authors Mustapha Si Tayeb