Weightless Neural Network with Transfer Learning to Detect Distress in Asphalt

TL;DR

This paper presents a novel approach using a weightless neural network with transfer learning to automatically detect road distress, specifically holes and cracks, achieving high accuracy and outperforming existing methods.

Contribution

It introduces a transfer learning-enhanced Wisard weightless neural network architecture for road distress detection, demonstrating improved accuracy over state-of-the-art approaches.

Findings

Achieved 85.71% accuracy on street images from Brazil.

Demonstrated superiority over existing methods in distress detection.

Validated the approach with real-world street data.

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.