Deep Learning Approaches in Pavement Distress Identification: A Review
Sizhe Guan Haolan Liu Hamid R. Pourreza Hamidreza Mahyar
This paper presents a comprehensive review of recent advancements in image
processing and deep learning techniques for pavement distress detection and
classification, a critical aspect in modern pavement management systems. The
conventional manual inspection process conducted by human experts is gradually
being superseded by automated solutions, leveraging machine learning and deep
learning algorithms to enhance efficiency and accuracy. The ability of these
algorithms to discern patterns and make predictions based on extensive datasets
has revolutionized the domain of pavement distress identification. The paper
investigates the integration of unmanned aerial vehicles (UAVs) for data
collection, offering unique advantages such as aerial perspectives and
efficient coverage of large areas. By capturing high-resolution images, UAVs
provide valuable data that can be processed using deep learning algorithms to
detect and classify various pavement distresses effectively. While the primary
focus is on 2D image processing, the paper also acknowledges the challenges
associated with 3D images, such as sensor limitations and computational
requirements. Understanding these challenges is crucial for further
advancements in the field. The findings of this review significantly contribute
to the evolution of pavement distress detection, fostering the development of
efficient pavement management systems. As automated approaches continue to
mature, the implementation of deep learning techniques holds great promise in
ensuring safer and more durable road infrastructure for the benefit of society.