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Over the last few years, vision-based approaches have augmented RCM solutions and remarkably advanced the development of pavement monitoring and analysis. It is imperative to note that there are other existing reviews [15,16,17,18] addressing pavement distress studies, as discussed above. These review articles largely emphasize the types of pavement distresses, the detection equipment used, the sensor technologies available, and various computer vision techniques employed in the detection of cracks and potholes in pavements; however, they do not focus on the data-driven algorithms used for distress detection. The review on pavement-defect detection methods based on DL by [16] provided a substantive overview of the subject, but has a restricted scope. The proposed review in this paper aims to provide an all-inclusive review by focusing on the next-generation sensing technologies and associated AI-based RCM methods, by elucidating the methodologies and challenges in current developments, as well as recognizing the prevailing research voids for further research studies. Therefore, the criteria for consideration of research articles in this paper are the evaluation of the existing sensor-based and AI approaches deployed on different platforms, namely UAVs, ground vehicles and smartphones for RCM. AI is a data-driven amalgamation of various ML and DL algorithms inherently dependent on sensors and data acquisition to provide solutions to real-world problems. These algorithms are a subset of the entire AI domain and have been shifted from handcrafted feature extraction-based ML methods to automated DL methods. To help researchers and engineers better understand the application of AI methodologies in pavement monitoring and analysis, the current review summarizes the recent work that has been established from the year 2017 to 2022. The AI approaches are classified under sensor-based methodologies, along with the application of machine learning and deep learning algorithms for RCM. An exhaustive review of the DL methodologies is presented that builds on classification, segmentation and detection, employing next-generation sensors integrated with different data acquisition platforms.

Ref. [69] proposed a conditional Wasserstein Generative Adaptive Network (cWGAN) [70] based method, ConnCrack, to the inspect cracks on road surfaces using a cost-effective commercial grade sports camera. This method performed pixel-level crack detection and provided a novel algorithm based on a depth-first search to determine the optimal crack connectivity map. A pixel-level annotated dataset, EdmCrack600, with 600 images was created for public use by the authors. However, in this study, the pixel-level masks were not transformed to the physical properties, such as the width or length of the cracks. Additionally, the proposed method was a data-starving model with training on three datasets (ImageNet, Crack Forest Dataset, and EdmCrack600). Ref. [71] proposed CrackU-net, a crack extraction method from pavement images, regardless of noise levels, background conditions and image quality. The method was based on deep neural networks for pixel-wise crack detection. CrackU-net was trained on images collected by high-speed vehicle-mounted cameras and smartphones and exhibited a high accuracy of 99%.

Ref. [93] presented an RCM method based on Faster R-CNN to recognize and locate cracks, potholes, oil bleeding and dot surface autonomously. A total of 20 Faster R-CNNs were trained and tested on 6498 pavement images, with the performance of the optimal one having accuracy rates, recall rates and location errors of 90.4%, 89.1% and 6.521 pixels. In comparison to the CNN and K-value method, the optimal Faster R-CNN located pavement distresses with more precision [94] proposed a methodology for automatic pavement image distress detection and classification using CNN models and a low-cost vehicle-mounted high-definition camera. The pavement distress types were categorized as linear or longitudinal crack, network crack, fatigue crack or pothole, patch, and pavement marking. The detection rate and classification accuracy of the proposed approach with the trained CNN model reached 83.8% over the test set. A sensitivity analysis was also carried out for evaluating the different regularization techniques and data generation strategies.

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