Work

Weightless Neural Network with Transfer Learning to Detect Distress in Asphalt

Year: 2018

Type: article

Abstract: 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.

Source: International Journal of Advanced Engineering Research and Science

DOI: 10.22161/ijaers.5.12.40

Authors: Suayder Milhomem, Tiago da Silva Almeida, Warley Gramacho da Silva, Edeilson Milhomem da Silva, Rafael Lima de Carvalho

Institution: Universidade Federal do Tocantins

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W2906820832