Heavy vehicle weights must be closely monitored to prevent fatigue-induced deterioration and critical fracture to civil infrastructure, among many other purposes. Developing a cost-effective weigh-in-motion (WIM) system remains challenging. This doctoral research describes the creation and experimental validations of a computer vision-based non-contact vehicle WIM system.
The underlining physics is that the force exerted by each tire onto the roadway is the product of the two key vehicle parameters: tire-roadway contact pressure and contact area. Computer vision is applied (1) to measure the several tire parameters so that the tire-roadway contact area can be accurately estimated; and (2) to recognize the marking texts on the tire sidewall so that the manufacturer-recommended tire pneumatic pressure can be found. Consequently, a computer vision system is developed in this research.
The computer vision system comprises a camera and computer vision software/techniques for measuring the tire parameters and recognizing the tire sidewall markings from individual tire images of a moving vehicle. Computer vision techniques, such as edge detection and optical character recognition (OCR), are applied to enhance the measurements and recognition accuracy. Numerous laboratory and field experiments were conducted on a sport utility vehicle and fully loaded or empty concrete trucks to demonstrate the feasibility of this novel method. The vehicle weights estimated by this novel computer vision-based non-contact vehicle WIM system agreed well with the curb weights verified by static weighing, demonstrating the potential of this computer vision-based method as a non-contact means for weighing vehicles in motion.
To further illustrate and exemplify the versatility of this novel computer vision-based WIM system, this research explores the potential application capability of the system for structural health monitoring (SHM) in civil engineering. This work aims to investigate the potential of this proposed and prototyped computer vision-based vehicle WIM system to acquire vehicle weight and location information as well as to obtain corresponding bridge responses simultaneously for later structural model updating analysis and damage detection and identification framework. In order to validate the concept, a laboratory vehicle-bridge model was constructed.
Subsequently, numerous experiments were carried out to demonstrate how the computer vision-based WIM system can be utilized as a resourceful application to (1) extract bridge responses, (2) estimate vehicle weight, and (3) localize the input force simultaneously. This doctoral research delivers a unique, pioneering, and innovative design and development of a computer vision-based non-contact vehicle WIM method and system that can remotely perform vehicle weight estimation. It also demonstrates a novel application of computer vision technology to solve challenging weigh-in-motion (WIM) and civil engineering problems.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/62s2-gf28 |
| Date | January 2022 |
| Creators | Leung, Ryan |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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