3D laser-based pavement imaging systems have been widely adopted by state departments of trans- portation (DOTs) in the last decade for automated pavement condition survey (APCS) assessment; 2D imaging technologies and smartphones are also used to perform pavement condition evaluations, especially for local transportation agencies. Collected pavement images are then used to semi- or fully automatically extract pavement distresses through various methods. Among these methods, models based on artificial intelligence (AI) with machine learning and deep learning (ML/DL) have gained much attention for pavement distress identification in the last several years. However, most AI models either are not yet fully integrated with how state DOTs use the pavement distress data or have not been sufficiently developed to employ quality 3D pavement image data.
The collected distresses, such as cracking, faulting, flushing, and raveling, are key indicators of triggering pavement maintenance and rehabilitation activities. Without a clear understanding of state DOTs’ ultimate use of this distress data, AI model development efforts for distress detection and/or classification, which include AI model formulation, distress annotation, training, and performance evaluation, could be misguided and fail to reach their full potential. For example, the AI-based models for automated crack detection using the classification of image blocks with cracking distress may not be able to output accurate cracking length and width information. Therefore, the outcome produced by the model may not meet the state DOT’s need for project-level applications, such as planning crack sealing projects. Alternatively, the performance of supervised learning AI models for automated pavement distress extraction relies heavily on several factors, including the quality of the pavement image data used, data size and diversity, the annotation quality (labeled ground truth distresses), the model formula- tion, model training, and so forth. However, the performance evaluation method used for many developed models is not always clear, especially for the diversity of the data used for that evaluation and its established ground truth. This ambiguity makes comparing the performance of different models challenging and unreliable.
The objective of this synthesis was to document current state DOT practices for both automated pavement distress identification and AI (ML/DL) technologies for pavement condition evaluation. Information for this study was gathered through a literature review, a survey of state DOTs, and follow-up interviews with selected DOTs. However, the relatively recent development and implemen-tation of 3D technology and the use of AI for APCS analysis resulted in difficulties identifying any state DOT that can to provide details and specifics for case example development. Therefore, in lieu of case examples, the report provides a general summary of efforts made for AI model development and training.
Linda M. Pierce, Sarah E. Lopez, Jose R. Medina, and Vivek Jha of NCE collected and synthesized the information and wrote the report. The members of the topic panel are acknowledged on page iv. This synthesis is an immediately useful document that records the practices that were acceptable within the limitations of the knowledge available at the time of its preparation. As progress in research and practice continues, new knowledge will be added to that now at hand.
