Smarter Road Inspections: AI-Enhanced 3D Imaging for Pavement Damage Detection

AI-driven 3D imaging technologies are transforming pavement damage detection by enhancing accuracy, efficiency, and automation in road inspections. However, challenges such as high costs, lack of open datasets, and multi-distress detection limitations must be addressed for widespread adoption.

Researchers from the University of Sharjah, UAE, in collaboration with the Sustainable Civil Infrastructure Systems Research Group and the Research Institute of Sciences and Engineering, have conducted a comprehensive systematic review on the use of artificial intelligence (AI) and three-dimensional (3D) imaging for pavement damage detection. The study, covering 85 research contributions published between 2011 and mid-2024, highlights how AI-driven methods are revolutionizing road maintenance, making inspections more efficient, accurate, and cost-effective. Traditional pavement inspection techniques, including manual inspections and accelerometric sensors, are time-consuming, expensive, and prone to human error. As a result, AI-powered 3D imaging technologies, such as laser scanning, stereo cameras, and infrared sensors, have emerged as advanced solutions that enable accurate depth estimation and geometric characterization of pavement distress. These AI-driven approaches offer a transformational shift in road infrastructure monitoring, reducing the risk of road hazards and optimizing maintenance efforts.

The Rise of AI in Pavement Damage Detection

AI has significantly improved pavement damage assessment, with Convolutional Neural Networks (CNNs) emerging as the dominant model, representing 74.1% of contributions. CNNs have proven to be highly effective in detecting cracks, potholes, and other pavement deformities by automatically extracting key features from images. Other machine learning techniques, including Artificial Neural Networks (ANNs) (4.7%), Random Forest (RF) and Support Vector Machines (SVM) (10.6%), and Recurrent Neural Networks (RNNs) (1.18%), have also been explored. Among them, CNN architectures without pooling layers have demonstrated the highest precision in detecting fine cracks and texture-related distress. The study categorizes AI-based pavement assessments into five primary application areas: crack detection (34.1%), texture/friction/raveling estimation (25.88%), pothole/manhole identification (18.8%), multiple distress detection (16.47%), and rutting evaluation (4.7%). These AI models are further enhanced by various 3D data pre-processing techniques, such as data augmentation, noise filtering, and depth extraction, which help refine and optimize detection accuracy.

3D Data Collection: Technologies and Challenges

The review identifies laser scanners as the most widely used 3D data collection method, appearing in 53% of the studies, followed by stereo cameras (15.3%), single digital cameras (9.4%), and infrared-based imaging systems (5.88%). While laser scanning is highly accurate, its high cost and maintenance requirements make it impractical for widespread use. Researchers have thus been investigating alternative low-cost depth estimation techniques, such as stereo imaging and structure-from-motion, which allow for depth mapping using standard cameras rather than expensive laser scanners. The review also highlights the growing interest in integrating AI-powered pavement detection with drones (Unmanned Aerial Vehicles – UAVs). This integration could significantly enhance mobility, accessibility, and scalability in road inspections, especially for remote or hard-to-reach areas. However, challenges persist, including varying lighting conditions, occlusions, and environmental factors that impact the accuracy of AI-based assessments. More research is needed to develop robust algorithms that can adapt to different imaging conditions and environments.

Addressing the Data Gap: The Need for Open-Source Datasets

A critical challenge in AI-driven pavement damage detection is the lack of publicly available 3D pavement datasets. The majority of studies rely on self-collected data, making it difficult to benchmark and compare AI models effectively. The absence of standardized datasets limits the generalizability of AI techniques across different regions and road conditions. Researchers emphasize the urgent need to develop an open-source 3D pavement damage dataset, which would facilitate fair comparisons between various AI approaches and accelerate advancements in road infrastructure automation. Additionally, high computational costs remain a major challenge, as deep learning models—particularly CNN-based architectures—demand significant processing power and large amounts of labeled data. Some studies have addressed this issue by employing transfer learning, data augmentation techniques, and hybrid AI models, such as Genetic Algorithms combined with Neural Networks, to optimize model performance.

The Future of AI-Powered Road Inspections

The review identifies several promising future research directions that could enhance the effectiveness of AI-driven pavement assessment. One significant gap is the limited exploration of sequence-based learning models, such as Long Short-Term Memory (LSTM) networks, beyond crack and rutting detection. Expanding their use to detect potholes, manholes, and multiple distress types could further enhance AI’s ability to analyze complex road conditions. Another avenue for improvement is using Generative Adversarial Networks (GANs) to generate synthetic training data, addressing the issue of insufficient labeled datasets. Additionally, advanced 3D data pre-processing techniques, such as Hough Transform, super-voxel clustering, and voxel-based growing methods, could significantly improve AI model accuracy. Researchers are also exploring hybrid AI models that combine CNNs with reinforcement learning or evolutionary algorithms to enhance robustness and adaptability.

Another pressing research challenge involves enhancing AI models to detect multiple pavement distress types simultaneously. Most current models are trained to identify only one type of damage, such as cracks or potholes, even though real-world roads often suffer from overlapping issues. Future AI architectures should focus on multi-class damage detection and cross-category classification, enabling more comprehensive road condition assessments. Moreover, the impact of environmental factors, such as lighting variations, road debris, and seasonal changes, remains an understudied area. Developing more adaptive AI models capable of functioning reliably in diverse environmental conditions would be crucial for real-world deployment.

Despite these challenges, the rapid adoption of AI-driven 3D imaging technologies is transforming the field of pavement damage detection. As research continues to refine AI models and data collection techniques, road inspections are shifting from labor-intensive, manual evaluations to data-driven, automated solutions. AI-powered systems not only improve detection accuracy and efficiency but also reduce maintenance costs and road hazards, ultimately leading to safer and more sustainable infrastructure. Given the increasing research interest and technological advancements, AI-based pavement assessment is poised to become the global standard for road maintenance. However, addressing the existing challenges in data availability, computational efficiency, and multi-distress detection capabilities remains essential to unlocking AI’s full potential in road safety and infrastructure monitoring.