We’ve teamed up with our partners at EyeVi Technologies to look at how the use of artificial intelligence (AI) can change the way highway infrastructure assets are managed.

Here they outline some of the key elements to consider:

Rapid developments in the research of artificial intelligence make it possible to transfer repetitive, complex, precision tasks from humans to machines. Moreover, using AI can speed up this process making it more cost-effective. The inspection of roads for deterioration is one such necessary yet repetitive, complicated process. Could a machine armed with AI and state-of-the-art hardware save personnel from this assignment?

The old-fashioned way is costly

Roads tend to deteriorate with time under the influence of traffic and pressure from the environment, such as varying weather conditions and ground movements. Efficient and timely road inspection is one of the key elements of a successful highway management system. However, periodical human visual inspection of the road surface tends to be costly and time-consuming. This is because the inspection has to be done quite slowly so that the human eye can detect possible defects. Secondly, two people are needed as humans are able to concentrate on only one task at a time. Driving a car is a highly complex and attention-demanding task and it’s not possible – and certainly not safe – to inspect the road simultaneously with driving a car. Finally, all the gathered data is entered manually in the system and only then it is possible to analyse it. However, with the help of an AI, this process and can be significantly streamlined and these costs can be substantially reduced.

How does AI work in defect detection?

The use of AI in defect detection is more and more widespread among companies that offer road inspection software. So, how does it work?

As with any AI-powered software, the AI is first trained on thousands of images to be able to distinguish the defective areas – such as potholes, edge defects, simple cracks, network cracking and weathering – from the acceptable road surfaces. For training, two types of input data are used: new input images and annotated input images. The AI learns from the manually annotated data so that it is able to distinguish different types of road defects on new data. When the training is finished, the AI-powered software is tested for success rate by comparing its detection results with the results of an experienced human observer. When the success rate is high enough, then the software is ready to be used. If it’s not, more training will take place to reach a higher accuracy level. When the training process has ended and the software is ready to be used, all that needs to be done is to gather the input data that has to be analysed and let the AI processing do the rest of the work.

One of several approaches to gathering input data for defect detection is using smartphones. With this method, and depending on the company, road officers just have to download an application, position a smartphone on the windshield, and the defect detection process can be started. Although user-friendly and simple to use, there are downsides to this kind of approach.

The downsides of the smartphone-based approach

Due to their relatively small size, smartphones also have small image sensors. Small sensors mean poor resolution and noise in low-light or other difficult shooting conditions. In addition, their geo-data is quite inaccurate as they generally use AGPS to locate their position. This, for example, can cause a horizontal position error of 7–13 m in an urban environment (tested on iPhone 6). Thus, while smartphones can be sufficient for simple data collection, they lack in location accuracy and image quality to be truly effective. This approach therefore creates problems as for truly valuable outcomes, the input data has to be of the highest quality.

How do we do it?

In the UK, EyeVi Technologies and XAIS Asset Management have joined forces to offer XAIS iV.

This is an AI surface defect detection and data management solution that consists of several submodules. The input data is gathered using state-of-the-art technical apparatus that is portable and mountable to any type of car. The apparatus includes a 360° panoramic camera, LIDAR scanner, GNSS/INS system, and data collection computer with software. This allows our system to easily gather various types of high quality data at a highway speed.

Over 21,000 km worth of digitised road defect data was used to train the AI and now it can surpass the capabilities of manual detection and achieve constant and uniform quality level throughout the road network. Since the whole process is automatic, it speeds up the data gathering and analysis process and can heavily reduce the costs per km. But this is not all that can be offered. In addition, we can generates panoramic image and 3D point cloud data that can be used to detect various aspects of road furniture such as traffic signs, road barriers, fences, and profiles. All data is then uploaded and accessible in the XA© Asset Management System to gives users an unparalleled overview of current stock and condition.

 

Available now, it is a solution that will change the future of highways infrastructure asset management.