Many older bridges and tunnels were constructed using standards by now many decades out-of-date, at a time when trucks and other large vehicles were smaller. A bridge or tunnel strike is an incidence in which a vehicle, typically a lorry (truck) or double-decker bus, tries to pass under a bridge or tunnel that is lower than its height, subsequently colliding with the structure. These strikes lead to an increased cost of bridge repairs, clogged up roadways and increased potential for catastrophic events: hazardous spillage and/or total collapse. Today, Network Rail reports on average a strike every 4.5 hours. There are a number of reasons why strikes occur, and why drivers of heavy goods vehicles sometimes fail to recognise the warning signs, consequently striking the bridge or tunnel. At first glance, it may seem like the problem is a fairly easy one to solve; however, no matter how well planned the road system, human error is an ever-present risk. The research proposes to address the problem of bridge and tunnel strike prevention and management. The intent of the research is to develop an affordable, reliable and robust early warning over-height detection system bridge-owners can implement at locations with high strike occurrences. The research aims to test and validate a novel vision-based system using a single camera to accurately detect over-height vehicles using a set of optimised parameters. The system uses a camera installed at the offending height, which acts as an “over-height plane” formed by the averages of the maximum allowable heights across all lanes in a given traffic direction. Any vehicle exceeding this plane is analysed within a region of interest using a trigger-based approach for accurate detection and driver warning. If the vehicle is deemed to be over-height, a warning is issued to the driver. As a result, prolonging life expectancy of structures while decreasing the cost of repairs, maintenance and inspections.

Over-height trucks are not only a hazard to the over-height trucks themselves, but they pose a threat to the bridges they come into contact with, and most importantly the other drivers on the road way when a collision takes place with a low clearance structure. Therefore, there is a need for an over-height detection system that is affordable yet also reliable. At this time there exist over-height detection systems using laser and infrared beam devices however, they are expensive. This high cost makes it impossible for Department of Transportations across the nation to implement these systems at all low-clearance headroom roadways. In this research a machine vision based system is proposed to detect the height of trucks and provide a warning for over-height vehicles. The height determination will be completed using line detection and blob tracking; these two methods will be overlapped where an upper point of the truck can be compared to a lower point on the ground. These 2D coordinates will then be translated into 3D world coordinates that will provide an approximation of the truck height. If the truck is over the set height then a warning will sound. The accuracy of the test proves that the method is a reliable method of height determination, achieving a 96.59% accuracy rate for measured trucks. The method does have an error rate of 3.3%. The merit of this work is the creation of an automatic image based method which can provide height determination of trucks and is a low cost alternative to the current expensive laser and infrared detection systems.