Exploring the Efficacy of Sparse Feature in Pavement Distress Image Classification: A Focus on Pavement-Specific Knowledge

Abstract

Road surface deterioration, such as cracks and potholes, poses a significant threat to both road safety and infrastructure longevity. Swift and accurate detection of these issues is crucial for timely maintenance and user security. However, current techniques often overlook the unique characteristics of pavement images, where the small distressed areas are vastly outnumbered by the background. In response, we propose an innovative road distress classification model that capitalizes on sparse perception. Our method introduces a sparse feature extraction module using dilated convolution, tailored to capture and combine sparse features of different scales from the image. To further enhance our model, we design a specialized loss function rooted in domain-specific knowledge about pavement distress. This loss function enforces sparsity during feature extraction, guiding the model to align precisely with the sparse distribution of target features. We validate the strength and effectiveness of our model through comprehensive evaluations of a diverse dataset of road images containing various distress types and conditions. Our approach exhibits significant potential in advancing traffic safety by enabling more efficient and accurate detection and classification of road distress.

Keywords

• machine learning
• pavement distress
• intelligent transportation systems