An image-based convolutional neural network system for road defects detection

Mohamed Anis Benallal, Mustapha Si Tayeb

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.

Keywords


Convolutional neural network; Defects detection; Image recognition; Road damages; You only look ones

Full Text:

PDF


DOI: http://doi.org/10.11591/ijai.v12.i2.pp577-584

Refbacks

  • There are currently no refbacks.


View IJAI Stats

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.