An Universal Crack Detection Framework for Intelligent Road-Perceptive Vehicles

Abstract

Crack detection is essential for ensuring road safety and preserving the integrity of infrastructure. As intelligent transportation systems mature, vehicles equipped with crack detection capabilities have become more sophisticated. Traditional methods for detecting cracks have relied on designing complex networks, which can be time-consuming and may obscure the task's underlying simplicity, posing challenges for researchers and hindering integration into road-perceptive vehicles. This paper introduces an innovative, universal framework for crack detection that circumvents these issues by utilizing existing pre-trained segmentation methods with visual prompts. We introduce the visual crack prompt (VCP) mechanism, which refines the focus of pre-trained models on high-frequency features, significantly improving their ability to identify and segment specific crack features. Additionally, we present the diverse crack detection 1 K dataset (DCD1K), comprising 1000 images of 16 different crack types, to validate the VCP mechanism's effectiveness. Our experimental results showcase the framework's outstanding performance across six distinct datasets, highlighting its potential to revolutionize crack detection methods.