7 Job-ready AI Applications in Construction

Ready to streamline AI product deployment right away? Check out:

• V7 Model Training

• V7 Workflows

• V7 Auto Annotation

• V7 Dataset Management

Personal Protective Equipment (PPE) detection

Here’s the harsh truth—

PPE can save lives... but only if people actually use it.

According to NIOSH, there are 2,000 work-related injuries every day in the US that could be prevented through proper use of PPE.

Construction managers only have two eyes and a finite amount of time, so it can be tough to make sure everyone is using appropriate protective equipment. But this is where computer vision techniques such as object detection can make a big difference.

Check out YOLO Object Detection Guide.

Deep learning has made it possible to reliably detect a wide variety of protective equipment, from hard hats to safety goggles, gloves, high visibility jackets, and ear protectors. And with more training, the capacity for AI to identify safety equipment – or its absence – will only grow.

This means that job site cameras can act as additional eyes to help construction management ensure the safety of their employees.

A good example of construction safety software in action comes from this study, in which a new computer vision system was developed to detect construction workers’ posture and PPE in real-time. The system was trained with a dataset of 95 videos from the construction site cameras and learned to detect eight different kinds of PPE and nine different types of posture.

When tested on new video data, the model showed a lot of promise, achieving high-performance results in both identification and recall ratios over 83 and 95%, respectively.

Workplace hazard detection

While monitoring PPE compliance can significantly improve workplace safety at a construction site, it can’t ensure workers’ safety all the time. Looking out for workplace hazards themselves is another area that can benefit from support in the construction industry.

According to the Health and Safety Executive in the UK, a total of 142 workers were killed on the job in 2019/2020 and 693,000 sustained an injury at work. Further, the Labour Force Survey estimates that the cost of injury and ill health due to construction site working conditions was £16.2 billion in 2018/2019. 

Clearly, keeping construction workers safe is a big job with serious stakes. But once again—

AI is here to help!

AI-based computer vision solutions have been developed that can track, predict, and prevent workplace accidents from happening. Yes, this seems like a tall order, but here’s how it works:

Techniques such as object tracking can be used to identify construction workplace hazards like flammable materials or to monitor employees in hazardous zones. The AI models can then provide real-time warnings to managers and workers on-site.

Platforms such as IRIS are a great example of how this works in practice. They’re already leveraging AI integration to accomplish their mission of “zero accidents at workplaces”. 

IRIS’s AI creates a virtual map and analyzes the entire working area using CCTV camera feeds. It’s then able to identify hazards and configure real-time notifications and alarms on WhatsApp, SMS, buzzers, or PA systems any time a workplace hazard poses a threat to a worker.

For example, using object detection models, where the objects are annotated using bounding boxes, IRIS can identify forklifts and observe their movement in a given area. The models then raise an alarm if a forklift is moving too fast, going in the wrong direction, or parked in the wrong place. 

Looking for the perfect bounding box tool? Check out 9 Essential Features for a Bounding Box Annotation Tool.

AI models can also identify fires earlier than traditional detection systems, and even determine how many people are in danger and their locations. 

But computer vision can do more than just detect external hazards. 

Construction workers can easily injure themselves with improper lifting or other manual handling procedures. AI models can use techniques such as human pose estimation to monitor worker posture and pose, raising warnings if workers are at risk of injuring themselves.

IRIS isn’t the only construction safety AI in town, though.

Everguard complements computer vision with wearable technology and sensor fusion to improve worker safety with PPE detection, fall detection, forklift safety, anti-collision, crane detection, and posture detection.

If all this AI supervision sounds rather intrusive, consider one of V7’s users, IntenseEye. Their AI models also monitor the workplace and detect employee health and safety incidents, but they prioritize privacy as well as safety. 

IntenseEye’s visual data is processed and then deleted so that they don’t store any personal information. And their algorithm uses body features to identify human “objects” instead of facial recognition. In fact, their AI models are even designed to ensure anonymity by blurring the faces of workers in output data.

So, there is no need to fear that Big Brother is watching over the workplace. Safety is job #1 for this AI.

Corrosion detection

Keeping construction workers safe through PPE and workplace hazard monitoring is just one of the ways that AI can make job sites safer and more efficient. 

AI techniques like image classification and object detection can also be used to detect corrosion, which is a serious threat to construction operations and human safety.

Failing to identify corrosion can lead to machines and structures breaking down, leading to higher maintenance costs, environmental damage, injuries, and even lost lives.

Currently, certified inspectors tour construction sites to look for corrosion. But because this method relies on human judgement, the results are often inconsistent, subjective, and prone to error. With so much at stake, consistent, objective detection of corrosion can be a game changer for job site managers.

As you might expect with tasks involving visual inspection, AI and ML have some solutions to offer.

To this end, companies like Infosys are already developing neural network-based AI solutions to reliably and consistently find and classify corrosion. 

But can AI really detect such small, gradual processes?

Yes! 

First, the algorithm uses binary classification to identify different kinds of corrosion. Two important distinctions are between coating loss (when the protective coating on a metal like steel begins to break down) and actual rust. 

Next, it performs multiclass severity classification to determine the severity of corrosion.

Infosys trained its algorithm with a 70% test-train split on a data set that they labeled to create 3,000 annotations. They then used four annotation classes to differentiate levels of coating loss severity: P1, P2, P3, and normal. 

Even with this relatively small data set, Infosys was able to train their model to identify and classify coating loss with 70% accuracy! 

Looking for the perfect image annotation tool? Check out our list of the 13 Best Image Annotation Tools

Infosys also notes that with training on more visual data, the algorithm’s accuracy will only improve. This points to the importance of collecting large sets of relevant data, which is, fortunately, an easy task. In this case, training images can be captured by simply snapping photos of corrosion with a digital camera or drone.

Infrastructure asset inspection

Moving on from safety, AI computer vision is also being developed to inspect infrastructure assets on construction sites.

As with corrosion detection, this kind of inspection traditionally depends on human observation and judgment. While subjective human assessment can certainly work, it also has a tendency to be inconsistent and unreliable, which wastes time and resources.

Computer vision systems, on the other hand, can support human inspectors in making more objective assessments. 

Remote monitoring of job sites allows for more real-time, up-to-date construction tracking data on infrastructure assets and helps construction managers keep on target with the building information model (BIM) schedule. 

These AI systems not only track the progress of a construction project as a whole, but can also identify individual assets and track user information, maintenance requirements, and physical location as well. 

This is a powerful feature, because if any specific part of a construction project is experiencing cracks, concrete spalling, leakages, or other types of damage, object detection algorithms will find the problem and inform asset inspectors and construction managers immediately.

So what does this look like on the job?

Pipeline Inspection

Researchers in this study used Deep Neural Networks to automate the detection and classification of pipeline external damage. 

Remotely operated vehicles (ROVs) and drones captured survey videos of the pipeline, which researchers then broke down into frames with optical character recognition (OCR)-extracted time/location stamps. Pipeline inspectors then helped annotate these images to precisely mark and categorize damaged areas and their boundaries. 

Once a deep convolutional neural network architecture was trained on all this data, it was deployed to do its own test inspections.

And the results did not disappoint.

The AI model managed an average precision of 86%. While not perfect, this is on par with the performance of human inspectors, only much faster. 

You can use V7 for your OCR projects. Take advantage of our public Text Scanner model to perform any OCR tasks.

Defect Detection in Concrete

Very small cracks, leaks, and dents in concrete structures can cause big problems later on if left untreated. Think about how freezing water can turn small cracks in a street into massive potholes. 

Computer vision systems combined with camera and drone technology are amazingly well suited to identifying and reporting these types of defects. Techniques such as object detection, image classification, and image segmentation can be used to detect and measure defects in as small as 0.01% of an image.

Using these techniques on images of buildings, bridges, roads, pipes, or tunnels can help construction managers make building and maintenance more efficient by predicting future conditions, supporting investment planning, and allocating targeted maintenance and repair resources. 

For example, in this study researchers investigated how well unmanned aerial vehicles (UAVs) and deep learning could inspect concrete structures. They used 3,000 images from a massive data set of publicly available images, and divided them into two categories of “crack” and “no crack”. 

The proposed “AlexNet” CNN algorithm was then trained on 1,050 images and tested on 450 images. Amazingly, even with images that included noisy backgrounds, such as cracks with shadows and stains or cracks on rusty and rough surfaces, the algorithm achieved 98.4 % accuracy in crack detection!

Even More Infrastructure to Inspect

Similar technology and techniques are also being used for infrastructure inspection outside of construction sites. 

For example, the company Eye For Infrastructure uses its AI models to analyze the quality and deterioration of roads. These kinds of systems can also be used to monitor the infrastructure by detecting damaged structures, tree overgrowth, drainage problems, and downed power lines. 

Currently, AI computer vision models are helping inspect a wide range of infrastructure from tunnels to bridges, solar farms, cell towers, and hydroelectric dams.

Without these kinds of utilities in good working order, we’d all be in a world of hurt. So having AI assistance to make inspection and maintenance more efficient helps us all!