The present paper shows a solution to the problem of automatic distress detection, more precisely the detection of holes in paved roads. To do so, the proposed solution uses a weightless neural network known as Wisard to decide whether an image of a road has any kind of cracks. In addition, the proposed architecture also shows how the use of transfer learning was able to improve the overall accuracy of the decision system. As a verification step of the research, an experiment was carried out using images from the streets at the Federal University of Tocantins, Brazil. The architecture of the developed solution presents a result of 85.71% accuracy in the dataset, proving to be superior to approaches of the state-of-the-art.
@article{arxiv.1901.03660,
title = {Weightless Neural Network with Transfer Learning to Detect Distress in Asphalt},
author = {Suayder Milhomem and Tiago da Silva Almeida and Warley Gramacho da Silva and Edeilson Milhomem da Silva and Rafael Lima de Carvalho},
journal= {arXiv preprint arXiv:1901.03660},
year = {2019}
}