Transform Your Road Assessments With Automatic Defect Detection in Road and Pavement Assets and Generate Road Condition Inventories Quickly
Roads are our most important infrastructure assets and are critical for the safety and mobility of road users, including personal vehicles, bicycles and pedestrians. These assets are also important for local government agencies, road authorities, councils and road operators who are responsible for their maintenance, repair and overall condition. However, the management of road and pavement assets is a complex task and it can be challenging to identify and address defects in a timely and cost-effective manner.
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One of the key inspection challenges in managing road and pavement assets is the timely detection of defects, such as potholes, road surface cracks (longitudinal, transverse cracks etc.), alligator cracking and fading line marking. These defects can cause damage to all vehicles and bicycles and they can also pose a risk to road users. But when you understand Pavement Distress Interactions and how pavement faults spread, the importance of having a strategy to gather data more regularly and promptly identify defects early becomes clearer.
The effectiveness of various pavement rehabilitation treatments on a pavement’s service life is dependent on the timely identification of defects.
The optimal time to apply a treatment is not solely when the greatest improvement in condition is realised but when the greatest improvement in condition is realised at the lowest cost.
Put simply, deferring the consistent inspection and timely maintenance of road infrastructure will result in proportionately greater rehabilitation costs at a later date.
Traditional methods of defect detection, such as visual manual inspection, have several limitations and can be time-consuming, take considerable effort, are costly and prone to errors. Using Automatic Road Analyser (ARAN) vehicles equipped with lasers, whilst accurate, is very expensive and are thus used too infrequently to provide any real benefits as road conditions can deteriorate quickly between inspections which adds to the lifecycle costs of pavement maintenance. However, new and emerging technologies, such as artificial intelligence (AI)-based automated road damage detection (AI road maintenance), brings the opportunity to improve the efficiency and subjective consistency of defect detection (regardless of the type of road surfaces inspected) and to reduce the risks to end-users that are associated with road defects.
What is Automatic Road Defect Detection Using Machine Learning? An Intelligent Road Analyser.
Automatic road defect detection (or is sometimes referred to as an automated road inspection or automatic road analyser) using machine learning analysis should now be a critical component of road and pavement asset management and its visual inspection process to reduce the total lifecycle costs of our road infrastructure. It is an automated road surface inspection solution that uses AI-based machine vision feature extraction technologies to automatically identify and locate defects in road and pavement assets that was historically performed by human visual inspection. This is achieved by using digital cameras or mobile phones mounted on a vehicle’s windscreen during road inspections. The data collected by these cameras is then analysed using deep learning algorithms, which can identify and locate various road surface defect types and structural issues, such as potholes, cracks and fading line markings.
Maintain-AI provides an industry leading automated visual inspection system that uses Machine Learning technology to help users responsible for road network management inspect road surface quality to optimise the use of available funding, better communicate funding needs and more objectively assess, monitor and support the management of your entire pavement network; be it for pavement distresses and/or accounting for the performance of other associated assets and elements across the road network.
The Benefits of Automatic Defect Detection
Automatic defect detection using machine learning analysis within a trained AI model is a powerful tool for maintaining the safety and integrity of our roads and highways. By using machine learning and artificial intelligence, this technology is flexible and is able to quickly, consistently and objectively (less human error) identify a wide range of defects, including different types of road cracks (e.g. alligator cracking, longitudinal cracks, transverse cracks etc.), potholes and more.
Once image acquisition is finalised (through use of a dedicated and free APP) and uploaded to Cloud platform, Maintain-AI employs advanced computer vision and machine learning techniques to consistently identify and evaluate various types of road distress and related infrastructure elements from road network images. We believe in the evidence that consistent objective network inspections and regular preventative maintenance, regardless if we detect every single pavement issue or not, saves time and money.
Effective pavement and infrastructure management must have accurate and consistently collected data and this is one of the key benefits of automatic defect detection in its ability to provide early detection of road defects. This allows road operators, councils, local government agencies and maintenance contractors to repair problems before they become major issues. Early detection also helps to reduce the risk of accidents and injuries, which is especially important for bicyclists and personal vehicles. Moreover, adopting an automatic defect detection strategy will also help to improve overall customer satisfaction by identifying and addressing defects in a timelier manner.
Another benefit of automatic defect detection is its ability to quickly analyse large amounts of data and provide a detailed report of the condition of the pavement. This allows road operators and asset owners to make more informed decisions about maintenance and repair needs, as well as plan for future evaluations.
In addition to providing early detection and more accurate assessments, automatic defect detection using machine learning can also help to reduce costs and increase efficiency. By providing near real-time data and network-wide visibility, this technology can help to identify defects that may otherwise go unreported. This can help to reduce the annual shortfall for maintenance and planned asset renewal, which in turn can help to increase overall customer satisfaction.
When Working With a Limited Asset Maintenance Budget, the Goal is to Maximise the Value of Each Dollar. This is what we aim to do with Maintain-AI.
Good Roads Cost Less! Save the Good Roads.
Furthermore, the use of Maintain-AI’s automated pavement condition reporting and interactive report viewer with annotation and custom defects options can lead to better management of pavement and ancillary assets such as footpaths. Additionally, using machine learning-based technologies to continuously optimise the automatic detection of defects and the pavement condition score can lead to a more consistent evaluation of the network-wide condition of road and pavement assets and identify trends. This added data and analysis aids in making more informed decisions.
The use of automatic defect detection via machine learning is a powerful tool for maintaining the safety and integrity of our roads and highways. By providing early detection, accurate assessments and cost savings, this technology can help to reduce risks and improve the overall satisfaction of road users.
Maintain-AI is an emerging leader in automated defect detection that leverages Artificial Intelligence technologies. Our solution will analyse and prepare detailed assessments that will support in identifying pavements and network infrastructure which:
need no immediate maintenance and therefore, no immediate expenditures
need minor or routine maintenance and immediate expenditures to keep them ‘good’
demand preventive maintenance activities such as asphalt overlay, seal, etc.
need major rehabilitation or reconstruction
require more detailed inspection.
Compared to other current approaches to road maintenance, modern pavement network assessments using Maintain-AI provides a more viable, lower cost and more appropriate solution in contrast to traditional assessments.
Remember, good roads should cost less.
Let us know your thoughts?
Maintain-AI aspires to support Governments, other Road Asset owners and Industry professionals transform pavement and network assessments through AI-driven solutions. Founded on the principle that "Good Roads Should Cost Less", we harness the power of computer vision and machine learning to automate road surface inspections. Our state-of-the-art tools detect road defects and assess related infrastructure, enabling professionals to make data-driven decisions. By advocating for the optimal use of maintenance budgets, we emphasise that well-maintained roads are more cost-effective across a road's complete asset lifecycle. Our commitment is to support regular, objective network inspections, ensuring that every maintenance dollar is maximised. With Maintain-AI, infrastructure asset management is not only efficient but also offers a clear return on investment through maintenance savings. Join us in our mission to make roads better, safer, more sustainable and more cost-effective. All road users deserve it.