AI-Powered Pavement Crack Detection with Simerse
Cracks in asphalt pavements destroy the aesthetic and functionality of road networks. In extreme cases, they can even turn into potentially dangerous hazards that threaten the safety of road users.
So, finding and fixing these cracks in time is always a critical part of highway maintenance strategy. However, as road networks expand each day, infrastructure maintenance teams have trouble keeping up with them.
Fortunately, AI-based visual inspection tools are emerging as a solution to help detect cracks in pavements. This article will discuss the various types of pavement cracks, how AI can automatically identify them, and how you can rapidly deploy these AI systems. Let’s dive in.
Dealing With Expanding Road Networks
Highways and road networks are an essential part of the infrastructure of any town. Therefore, keeping them in good health and good shape benefits the economic health of the populace.
Such infrastructure determines how efficiently we can deliver our goods and services. Highways also control how well the workforce functions in their economic activities.
However, as these road networks expand, the costs of keeping and maintaining them in optimal conditions are skyrocketing. Moreover, road maintenance cannot be ignored as the cost of bad roads takes quite a toll on the economy.
So, public works departments face the considerable problem of inspecting and maintaining vast roads using old methods. Unfortunately, even when we ignore the costs, the accuracy and safety of old pavement crack detection methods leave much to be desired when we put them in focus.
Thankfully, breakthroughs in the field of Artificial Intelligence (AI) are coming just in time to clear these bottlenecks.
AI Visual Inspection for Pavement Crack Detection
AI visual inspection has plenty of applications in infrastructure maintenance, and pavement crack detection is one of them. Using the power of Computer Vision and Deep Learning, we can train machines to detect and classify cracks in asphalt pavements.
This new technology promises to revolutionize road inspection and maintenance. Standard approaches that employ tedious human effort like manual surveying or expensive techniques like ground scanning radar have proved unreliable or costly to deploy on a large scale.
AI visual inspection solves this problem by providing an accurate, affordable, and easily deployable platform for easy crack detection.
So, how does it work?
Pavement crack detection with AI is possible thanks to the union of computer vision and deep learning. First, a camera collects data by taking pictures of the pavement.
The image data is then fed into the deep learning model, which analyzes it for cracks, classifies the type of crack, and records it in a backend system. But, of course, this is an oversimplification of the process. So, let’s dive into the complexity of the system.
The brain of the entire pavement defect detection system is the deep learning model. To develop a deep learning model, we train an algorithm on thousands of labeled images of various cracks in pavements.
The deep learning model processes these images and understands the patterns that it can use to identify each type of crack. Then, these patterns are used to process the data that come into the system to find and classify the cracks.
Once the system’s ‘brain’ is complete, the deployment method is next. We can employ several methods for data collection, including drones, vehicles, etc., really any equipment that can capture images.
But in this case, the most cost-effective method is a survey vehicle. This method involves outfitting a car with a camera for data acquisition and GPS/GIS tools.
These cameras employ lights that accurately illuminate the area of interest for proper data collection. They continually record the pavement as the vehicle moves, creating a data stream for the deep learning model.
When a model detects a crack and classifies it, an onboard computer records it. Also, the GPS/GIS records the crack’s location accurately for future action.
So, like this, you can inspect asphalt pavements and detect cracks and other defects accurately without interfering with the local road users.
What types of Cracks Can AI Visual Inspection Detect?
Cracks in pavements come in different forms, with each one presenting a unique difficulty in detecting. Therefore, we must train the deep learning model on these different cracks to use an AI visual inspection system.
Let’s look at some of these cracks and their characteristics.
These types of cracks occur perpendicular to the centerline or the road’s direction. They occur due to the freezing caused by low temperatures, and they’re aggravated and increased by heavy traffic.
Lateral cracks are some of the easiest types of pavement cracks to detect using AI inspection. Since they run transverse to the direction of travel, it’s easy to isolate and classify them correctly.
Longitudinal cracks are cracks that run parallel to the centerline of the road. They are mainly due to improper construction of the road further exacerbated by environmental conditions.
Detecting Longitudinal cracks poses somewhat of a challenge for conventional methods. Due to their orientation and lane markings that can mask them on the roads, they can be challenging to detect.
However, AI Visual Inspection models rise to the test and can detect these cracks with high accuracy.
Alligator cracks are a network of localized networks of cracks covering an area. These pavement defects are called alligator cracks because they resemble the reptile’s skin.
Detecting and classifying these cracks usually posed a problem using previous methods because they usually covered pretty large areas. Historically, the nature of alligator cracks made it hard to map them accurately.
However, with AI visual inspection, road workers can easily detect and fix these defects.
What are The Benefits of AI-Based Visual Inspection for Pavement Crack Detection?
AI visual inspection has many advantages over other methods used for pavement crack detection. Thanks to the cutting-edge technology, it eliminates several bottlenecks and brings several benefits. Let’s look at them.
Gathering data on cracks in highway pavements can be dangerous when using methods like surveying. In addition, due to the high traffic volume, there is a significant safety risk to the Engineer gathering the data.
Using AI for pavement crack detection eliminates this risk. Everything used for surveying and detecting cracks is contained entirely in the survey vehicle. Therefore, you can gather the data reliably and efficiently without endangering workers.
Speed is usually a problem when gathering data on defects in asphalt pavements. Usually, the data collection is performed at less busy times, like at night when the road isn’t experiencing heavy traffic.
However, this can backfire on the data’s quality as the lack of light can influence the surveyor’s judgment.
However, using AI crack detection with a data capturing vehicle, you can comfortably gather quality data with proper illumination. Even better, you can do it without interrupting commuters on the road.
The cost of the workforce and equipment required to perform road inspections is quite large. Furthermore, when we add the economic costs incurred by road shut down to perform the inspection, it is clear that road maintenance imposes a significant economic cost.
AI visual inspection solves this by providing a cheap, scalable platform for performing these inspections at an affordable rate per mile.
One of the issues with expanding road networks is the unavailability of qualified professionals to perform road maintenance tasks. The lack of workers can hamper maintenance efforts and put a damper on road construction.
Fortunately, AI visual inspection is an easily scalable platform for automating road inspection. The deep learning model makes the decisions, negating the need for the highly trained eye of professionals, allowing regular workers to detect defects.
How can Simerse Help?
Deploying an AI system for Pavement crack recognition is no easy feat. To get it right the first time, you have to partner with a company that can provide a one-stop-shop for your needs within budget.
Simerse is here to help with that. To help streamline your AI-powered crack detection system, here are some of the services Simerse offers:
Datasets for model training
The accuracy and quality of your AI-powered inspection system all depend on the training data. Using wrong or insufficient data will result in the crack detection system producing the wrong results.
Simerse can provide thousands of perfectly-annotated images for training your AI system. We can even deliver synthetic data developed for specific use cases depending on the customer’s needs.
Pre-trained AI Models
Training deep learning models requires technical expertise and can take a lot of development time if not done correctly. So, if you’re looking to deploy your crack deployment system quickly and cost-effectively, this might not be ideal.
Simerse solves this model by providing pre-trained models for your system. Using our experience in the field, we can help produce a model that satisfies all your uses accurately.
Go with Simerse today, and let us introduce you to a smarter, faster and better way of doing things. Thanks for reading!