Another Area Where Technology Can Make a Significant Impact To Improving Road Safety.
As technology continues to advance, there are increasingly more opportunities to adopt innovation to improve our daily lives. One area where technology is making a significant impact is in road safety.
Road safety continues to be a critical issue in today's fast-paced world, with an increasing number of people relying on cars for transportation. With current advances in automated road inspection technology using computer vision as offered by the Maintain-AI system, road asset owners, governments and road maintenance professionals are now better equipped to make roads safer for end users. Computer vision is a rapidly evolving field that has numerous applications in the road safety industry. In this article, we will explore the benefits of computer vision in road safety and how it can improve the safety of our roads.
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What is Computer Vision?
Computer vision is a field of practice that focuses on enabling computers to interpret and understand visual data from the world, such as images and videos. It involves developing algorithms and techniques to enable machines to "see" and "understand" images and videos in ways similar to human perception.
Computer vision is often considered a subset of Artificial Intelligence (AI) because it involves the use of machine learning and other AI techniques to enable machines to automatically analyse and interpret visual data. AI encompasses a broad range of technologies that aim to enable machines to perform tasks that typically require human-level intelligence, including tasks related to perception, reasoning and decision-making. Computer vision is one of many AI technologies that are being developed to solve complex problems in fields such as healthcare, energy and robotics.
Analysing Road Condition Data
Automated road surveys utilising computer vision and machine learning can play a critical role in analysing road condition data. Once data is collected, typically from cameras and good quality mobile phones, specialised software is employed to process and analyse the data, providing useful insights on road conditions. The analysis encompasses a variety of factors including assessments of the road surface roughness, pavement condition, presence of potholes and cracking.
To begin the analysis, the collected data is processed using computer vision and machine learning algorithms to identify any potential defects on the road surface. This technology can provide a more broader analysis of road conditions compared to traditional approaches, allowing road owners or maintainers a simple way to determine the severity and density of identified defects.
Through using this innovative approach and prioritising the maintenance work that needs to be undertaken, agencies can better allocate their resources to support safer roads for everyone.
The data analysis also allows for the identification of trends over time, making it possible to foreshadow the rate of deterioration of the road surface. This information can be important for road agencies to plan their future maintenance and rehabilitation work.
Improving Road Safety with AI. What are the Advantages?
Advantages of Automated Road Surveys and Inspection Systems
Automated road surveys provide several advantages over traditional manual surveys. One of the most significant advantages is the speed and consistency with which the data is collected. Automated surveys can collect a broad range of data across a large network of roads quickly, providing road agencies with a comprehensive overview of the condition of their assets. By using automated survey solutions like Maintain-AI, road authorities can significantly increase the frequency of pavement evaluations they undertake but still remain within existing constrained maintenance inspection budgets. Win-win.
Another advantage of adopting an automated condition survey is the consistency of the data attained. Traditional manual surveys can be subject to human error and variations in the survey methodology, which can impact the accuracy of the data. Automated surveys provide consistent, objective and reliable data that can be used to make more informed decisions.
Automated Road Surveys using Computer Vision and Machine Learning also offer several advantages over road surveys that use laser scanning and LiDAR fitted vehicles. Firstly, they are more cost-effective as they don't require expensive hardware and software. Secondly, they are more flexible than surveys that use laser scanning and LiDAR and can cover a road network much more frequently, giving road owners better insight on actual road conditions as they can collect and trend more data over time.
How Does Computer Vision Technology Support Improvements to Road Safety?
Automated road inspection technology using computer vision can improve road safety in many ways. One way is through the detection, evaluation and measurement of pavement distress, including potholes, cracks and other damage. By using quality cameras and mobile phones mounted on a vehicle windscreen, the technology captures images of the pavement surface, which are then analysed to identify areas of concern – type, severity and density.
How can AI prevent road accidents? Through the detection, evaluation and measurement of pavement distresses, including potholes, cracks and other damage, computer vision machine learning can contribute to improving road safety.
Once the damaged areas are identified, the technology can be used to alert road maintenance professionals, who can then schedule repairs. This not only reduces the risk of accidents caused by damaged pavement but can also minimise the amount of time the road is closed for repairs, resulting in less disruption to traffic and in principle, safer roads.
Five (5) Key Benefits of Computer Vision Based Automated Road Surveys in Improving Road Safety
KEY BENEFIT 1
Detecting, Categorising and Tracking Road Network Issues and Identifying Negative Trends
By identifying pavement deterioration and notifying road maintenance specialists to plan repairs, automated road inspection technology can increase road safety. In order to prioritise repairs and slow the spread of future damage to the road, maintenance staff must rapidly and precisely detect road issues. This can lower the chance of accidents caused by pavement deterioration and improve road user safety overall.
Computer vision algorithms are also able to spot trends in poor road surface conditions by analysing the data gathered from automated road surveys. For example, if a particular road segment consistently shows high levels of cracking or potholes, this can be identified across historic data as a negative trend. As a result, road authorities may be better able to identify underlying issues consistently that are causing various road defect types to occur, for example, structural sub-base issues, that may not be readily apparent during occasional spot assessments, as well as how road surface conditions change in response to various factors such as weather, traffic patterns and maintenance activities. This information can be used to prioritise road enhancement projects that improve road surface performance and to positively influence maintenance and repair schedules.
KEY BENEFIT 2
As eluded to above, through the collection of more consistent and timely presented data on road conditions, road maintenance activities can be better planned and organised. This reduces the need for manual inspections, resulting in significant time and cost savings. For instance, by using computer vision technology, road maintenance professionals can identify the exact location and extent of damage, allowing them to target repairs more efficiently. This not only results in a more effective repair process, but it also minimises the time required to complete maintenance activities, thus reducing the overall cost of maintenance. This technology can also be used to monitor the effectiveness of road maintenance efforts and adjust maintenance schedules accordingly.
KEY BENEFIT 3
Significantly Improving Data Collection Frequency
An Automated Pavement Condition Survey using computer vision offers several benefits over manual approaches and inspections involving a survey vehicle fitted with laser scanners / LiDAR. With the diminishing knowledge of assessing road infrastructure needs with infrequent monitoring, regular data collection is essential. Manual inspections are time-consuming and labour-intensive and they can lead to errors due to human limitations.
Also in comparison, inspections involving laser scanners / LiDAR are significantly expensive and not practical for routine monitoring which is important to keep the good roads good. Moreover, evaluating road maintenance effectiveness without consistent data can have negative effects on road conditions, safety and user experience.
KEY BENEFIT 4
The Potential to Reduce Planned and Unplanned Disruptions
By automating the detection process and cutting down on the amount of time needed to check for damage to the pavement, ML technology used during an automated road survey can also improve the effectiveness of road maintenance workers. Road closures for repairs may be shortened as a result of better planning, which will lessen traffic congestion and potential impacts of driver, cyclist or other road users from travelling through road repair and reconstruction areas.
KEY BENEFIT 5
Identification of Other Critical Surface Irregularities and Non-Pavement Issues In Road Network Infrastructure and Services
Machine learning can recognise rutting, flushing, bleeding and other surface problems that may affect rider comfort. For instance, computer vision can identify corrugations that may affect vehicle ride quality and alert maintenance schedulers to potential problems with the road's subsurface conditions.
Road signs that may be challenging to view, particularly those damaged by sabotage or ordinary environmental wear, can be identified with the use of machine learning. For instance, computer vision can identify when a sign is covered in graffiti and notify maintenance staff so that it may be cleaned and returned to its original position.
Road markings can be analysed by machine learning algorithms to determine where they may be fading or difficult to see. For instance, using computer vision, road crews can be alerted to repaint a worn-out road marking. Moreover, road debris that might cause accidents, including falling branches or other objects, can be identified using machine learning. Computer vision can recognise when an obstruction is present and alert road operators to remove it when utilised in real-time applications.
How Automated Road Surveys Using Computer Vision Can Support the Goal of the International Road Assessment Programme (iRAP) to Save Lives by Eliminating High Risk Roads Throughout the World.
iRAP is a global road safety charity that assesses road infrastructure to identify ways to improve road safety. The organisation uses a standardised methodology to evaluate road safety, known as the "iRAP Star Rating", which assigns a series of star ratings to a road based on its safety features. The goal of iRAP is to reduce the number of deaths and serious injuries on the world's roads by making them safer for all users.
Automated road inspections using computer vision can provide valuable data to support iRAP's efforts to improve road safety. Computer vision algorithms can be used to analyse images or video footage of roads and identify surface deterioration and potential safety hazards such as potholes, uneven road surfaces or issues with light poles, guardrails etc. This data can then be used to inform and update iRAP's road safety assessments and identify areas for improvement.
One of the key benefits of using computer vision for road surveys is the speed and efficiency of data collection. Traditional road surveys can be time-consuming and expensive, requiring teams of surveyors to manually collect data on road infrastructure. Computer vision algorithms can be trained to automatically analyse large amounts of data, significantly reducing the time and cost of road surveys.
In addition, computer vision can provide more consistent data compared to manual surveys. Human surveyors may miss certain details or record inconsistencies on the road, whereas computer vision algorithms can potentially be programmed to identify even subtle changes in road conditions. This is likely to improve current road safety assessment approaches and support more targeted improvements to road infrastructure.
Overall, automated road surveys using computer vision have the potential to significantly improve iRAP's ability to assess road safety and identify areas for improvement. By providing more consistent and efficient data collection, computer vision can help iRAP achieve its goal of reducing the number of deaths and serious injuries on the world's roads.
Automated road inspection technology using computer vision has numerous applications that can help to improve road safety. Computer vision enables machines to automatically analyse and interpret visual data such as images and videos in ways similar to human perception. Through computer vision technology, pavement distress, including potholes, cracks and other damage, can be detected and categorised. This technology offers a more thorough and broader examination of actual road conditions, giving road authorities more data points with which to manage their road networks. By prioritising the maintenance work that needs to be performed, road maintenance teams can better allocate their resources to support the strategy of safer roads for everyone.
Using computer vision in an automated condition survey for roads is like having a GPS navigator that can warn you of roadblocks and other traffic hazards in advance. Just as a GPS can provide real-time updates on road conditions, computer vision can identify and report many issues with the road before they become a safety concern whilst reducing maintenance costs through a more simple and efficient means of consistent data collection.
An automated pavement condition survey offers several advantages over traditional manual surveys. They can reassign manual labour to more beneficial activities rather than mundane data collection tasks and provide more consistent and reliable data that can be used to make more informed decisions. Automated surveys can also collect data on a large network of roads quickly, providing road agencies with a comprehensive 'evergreen' overview of the condition of their assets when data is collected frequently. Additionally, computer vision technology can be used to monitor the effectiveness of road maintenance efforts and adjust maintenance schedules accordingly, resulting in a more effective repair process and deliver overall cost savings over the pavements total lifecycle.
The use of computer vision technology in road safety has shown positive results in the detection and categorisation of road network issues, streamlining maintenance, improving road network planning and reducing accidents. This technology has been proven to be more cost-effective, user-friendly and flexible than surveys that use laser scanning and LiDAR and it is equally adapted to different road conditions, road types and sizes.
With the implementation of computer vision technology in road safety, road asset owners, governments and road maintenance professionals are now better equipped to make roads safer for end-users. The information gathered by this technology can help road agencies plan for upcoming maintenance and rehabilitation projects, identify trends over time and possibly predict how quickly the road surface will deteriorate. By prioritising the maintenance work that needs to be performed, agencies can better allocate their resources to ensure safer roads for everyone and keep the good roads good.
Maintain-AI is on a journey to support road authorities make more informed decisions and help improve road safety but we only have some of the solutions. Let's work together.
Remember, good roads should cost less.
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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.