Weightless Neural Network with Transfer Learning to Detect Distress in Asphalt
This addresses road maintenance issues for infrastructure managers, but it is incremental as it applies an existing method to a new domain.
The paper tackled automatic distress detection in asphalt roads, specifically identifying holes and cracks, using a weightless neural network with transfer learning, achieving 85.71% accuracy on a dataset from a Brazilian university.
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.