VISION-BASED SMART MONITORING AND ASSESSMENT OF HIGHWAY PAVEMENT INFRASTRUCTURES

The concept of Intelligent Transport Systems (ITS) for Smarter Cities has been introduced to categorize advanced tools and solutions to facilitate intelligent and data-driven monitoring and management of the transportation system. Improvements in visual data collection and recognition offer promising potential for low-cost highway infrastructure health monitoring with mobility and non-destructiveness. However, innovative computer vision-based techniques take place against a backdrop of deteriorating highway systems. Traditional and current state of practices in the area continue applications involving laborious and time-consuming surveys conducted by trained human inspectors or high-cost sensor networks with limited performance and/or unsolved practical challenges. Moreover, the ASCE 2021 Infrastructure Report Card (ASCE, 2021) diagnosed that over 40% of public roads are in poor or mediocre condition and the increasing needs of enhancement and expansion for the system has result in a massive 786 billion USD of underfunding. Hence, adequate and low-cost monitoring and assessment methods for traffic and pavement are indispensable for bringing an optimal life-cycle operations and maintenance management of highway infrastructures. A highway pavement is expected to withstand ever-increasing numbers of heavy vehicles of different types. With the introduction of autonomous vehicles, the traffic loading impact is further increasing. As such, accurate vehicle counts with classification and load identification is a vital traffic monitoring task. Moreover, period assessment of pavement friction performance at the tire-road interaction has a pivotal role in developing the inventory of the condition of infrastructures at the highway network level.

This research seeks to improve the measurement quality and monitoring efficiency of current traffic monitoring and pavement friction assessment practices, taking the use of modern digital techniques and computer vision advances. To achieve this objective, the study is realized through three research procedures: (i) development of a vision-based highway traffic load monitoring method towards WIM quality control, (ii) a mechanistic-empirical investigation of locked wheel friction testing on highway horizontal curves, and (iii) an outdoor photogrammetric survey of pavement surface with texture roughness characterization towards accurate and contactless friction prediction.

The first task, the vision-based traffic monitoring method uses convolutional neural network (CNN)-based semantic segmentation and object tracking algorithms. This approach began with pixel-wise recognition and localization of vehicle axles and body components using a real-world traffic image. Based on parameters estimated by a target-free camera calibration, the recognition results are used to identify the vehicle in image at a fine-grained classification level. Finally, according to the vehicle time and location synchronization, the camera machine vision automates quality control of a local Weigh-in-Motion (WIM) system through information fusion.

In the second task a locked wheel skid tester (LWST) practicability on highway horizontal curves is investigated through installation of two consumer-grade action cameras. Simultaneous monitoring of LWST test tire sideslip angle and vehicle articulation angle are achieved using image processing techniques such as feature detector and optical flow. The vehicle testing speed and pavement geometric properties and their impacts on pavement friction test performance are evaluated through field experiments under distinct testing scenarios. In the end, data analysis is performed to discuss the test reliability on both theoretical and practical grounds.

The third task develops a synthetic environment for outdoor practices of high-quality 3D pavement texture scan using Structure-from-Motion. A photo-realistic computer graphics model of asphalt pavement surface is produced and virtually scanned using candidate image acquisition plans. Quality assessment of the corresponding 3D point cloud reconstruction models is performed to suggest an optimal pavement texture sampling rate for field measurements. Then, a field texture photogrammetric survey is conducted, followed by measurements of a comprehensive list of texture parameters using pavement surface reconstruction sampled at different spatial resolution levels over the micro-texture scales. Finally, three LWST friction number prediction models are implemented using the texture characteristics as predicators. And the model with best test performance is proposed with a conclusion of working range of texture sampling rate.