How We Built an AI Pipeline to Reduce Hazards, Prevent Accidents, and Minimise Delays on Construction Sites — Here’s What We Discovered

We built a video inspection pipeline integrating Segment Anything Model (SAM), YOLO, and computer vision techniques to detect manufacturing faults 🚧🔧 in the construction industry.
Here’s what we’ll cover in this blog:

• The current safety issues such as outdated inspection methods, human oversight, and the gaps in site supervision.
• How AI can enhance detection and prevent risks before they escalate.
• Ionio’s Advanced Monitoring Pipeline, leveraging YOLO and SAM to streamline safety checks, identify risks in real-time, and help compliance officers stay ahead of potential hazards. 🔍💡

What to Expect in This Blog Series

This is Part 1 of a two-part series:

• Part 1: We’ll cover the architecture, the technical challenges, and the capabilities of SAM and YOLO in construction safety.
• Part 2: A hands-on demonstration of the pipeline, with code walkthroughs and real-world examples. 💻📊

By the end, you’ll understand how we built a solution that detects construction faults at least 2x faster, more affordably, and with greater accuracy. ⚡💰

Who This Solution Is For

Whether you are a :

• Construction Company Executive: Looking to dramatically reduce accident rates and associated costs
• Site Safety Manager: Seeking real time, comprehensive site monitoring
• Project Manager: Aiming to minimize delays caused by safety incidents
• Worker: Who deserves the safest possible working environment

If this sounds like you, contact us to learn how we can help create a safer, more efficient construction environment.

Let's first understand the current state of construction and the causes behind these accidents.

The Problem at Hand : Why Construction Sites Need an Overhaul

March 15, 2023. 9:47 AM. Chicago.

A routine morning at the Harrison Tower construction site turned an accident when Mike Thompson, a 12-year veteran worker, fell three stories after stepping onto a seemingly secure platform. The morning inspection had cleared it. All safety protocols were followed. Yet the platform gave way.

The culprit?

A hairline crack that no human eye caught during routine inspections.

The cost?

Three broken ribs, a crushed ankle, two months of project delays, and $350,000 in damages.

But Mike was lucky – he survived.

This isn't just another construction site story. This is why we turned to AI. The goal here is not to replace any human operator but instead to detect flaws and cracks invisible to human eye while also predicting future issues.

What makes an industry dangerous? Is it the number of fatalities, the rate of injuries, or the constant exposure to risk? By any measure, the construction industry remains among the few hazardous industry.

Understanding the Risks

Construction sites are among the most dangerous workplaces in the world. The construction industry faces three critical challenges:

1. Outdated Inspection Methods: Human eyes can't catch everything. The potential hazards often go unnoticed until it's too late.
2. Regulatory Gaps: No specific government agency strictly monitors construction site safety. The process is often outsourced, leading to a distributed supervision.
3. Human Limitations: With double shifts, fatigue, and the scale of modern construction sites, even the most experienced compliance officers can miss critical safety issues.

The Fatal Four

OSHA identifies the following four categories as the main reason behind any construction failure, these are:

1. Falls (~38%): From unstable platforms and inadequate safety harnesses
2. Struck-by Incidents (~14%): Involving falling objects or moving equipment
3. Electrocutions (~5%): Due to exposed wiring and power line contacts
4. Caught-between Accidents (~5%): Including trench collapses and machinery incidents

When we talk about construction site accidents, the financial impact is staggering. Let's break down the top five causes and their associated costs:

These five categories alone account for 76.3% of all construction-related accidents, totaling a staggering $8.69 billion in costs.

We need systems that never tire, never blink, and catch what human eyes miss. This is why they integrating AI into this sector holds so much of importance.

Ionio’s AI Pipeline: Enhancing Construction Site Safety

In the construction industry, the "Fatal Four" accounts for the majority of fatal accidents on job sites.To address this, Ionio’s AI-powered monitoring pipeline helps in :

• detecting high risk situations in real time
• anticipating potential accidents before they occur
• significantly reducing the need for manual inspection

The pipeline leverages Ultralytics' YOLO11 for object detection and Meta’s Segment Anything Model (SAM) for precise instance segmentation.

How It Works: The Two-Part Approach

1. Real-Time Object Detection with YOLOv11

YOLOv11 is the heart of our pipeline, responsible for identifying key objects and potential safety risks in real time. This model is known for its speed and accuracy, making it an ideal choice for high-paced construction environments.

• What it Detects:
  • Workers and their Personal Protective Equipment (PPE) (e.g., helmets, safety vests).
  • Machinery and heavy equipment.
  • Hazardous areas (unmarked or improperly marked).

For example, during a test, with the help of YOLOv11 we identified a worker without a helmet near heavy machinery, which triggered an instant alert to the compliance officer, preventing a potential accident.

2. Precision Segmentation with SAM

While YOLOv11 excels at detection, SAM steps in for detailed segmentation. This precision allows the pipeline to:

• Mark hazardous zones pixel by pixel.
• Identify areas where equipment or machinery poses a risk.
• Segment out-of-bound zones for workers.

In one instance, SAM accurately marked scaffolding areas as no-go zones for workers without proper training, allowing for real-time guidance by compliance officers.

Now, let’s explore how it works 🏗️🚧.

Understanding the Role of YOLOv11 in Our Solution

YOLOv11, the latest in the "You Only Look Once" family of models, known for its high-speed, high-accuracy object detection.

Here’s why it’s critical for our solution:

• Capable of processing video streams at low latency, ensuring that the AI reacts quickly to dynamic changes.
• Trained to recognise construction-specific elements such as PPE (helmets, vests), machinery, hazardous zones, and safety signage.
• Can flag missing or improperly used PPE in real time, preventing safety violations before they happen

With YOLOv11’s optimized architecture, it can instantly detect multiple objects, categorizing them (e.g., workers, equipment, hazardous zones) and highlighting safety violations.

Deploying YOLOv11 for Object Detection

• Install Dependencies:

pip install -q ultralytics opencv-python

• Select Your YOLOv11 Model: YOLOv11 comes with a range of pre-trained models to match different detection needs, each balancing accuracy and speed. Since I can’t share our custom model for confidentiality, I’ll show you how it works using YOLO11x, the most accurate model. Below are some options you can experiment with for your own projects:

• Running YOLOv11 for Object Detection:

To see YOLOv11 in action, you can run the following command on an example image sourced from a construction environment:

!yolo detect predict model=yolo11x.pt source='/content/pexels-rezwan-1078884.jpg'

Before Detection: The input image, showcasing a typical construction scene, is captured.

After Detection: YOLOv11 processes the image and highlights the key safety concerns (missing PPE, proximity to hazardous machinery, etc.):

This real-time monitoring solution acts as the backbone of our construction safety pipeline, instantly identifying deviations from safety standards and notifying compliance officers on the go.

Let’s now understand SAM’s role in our solution.

Segment Anything Models (SAMs) in Our AI Powered Pipeline

Segment Anything Model (SAM) is a state-of-the-art AI model designed for image segmentation. At their core, SAMs are designed to identify and segment objects within an image based on prompts—whether it’s a point, box, or mask. This ability to segment "anything" has significant implications for improving safety and efficiency on construction sites.

How Ionio’s SAM-powered System Works

SAM consists of three key components:

1. Image Encoder: A powerful vision transformer that processes and understands the construction site imagery.
2. Prompt Encoder: Processes various types of inputs (points, boxes, text) that indicate what needs to be segmented.
3. Mask Decoder: Generates precise segmentation masks for identified objects or areas of concern.

How Our AI Pipeline Enhances Construction Safety

Whether it's a small project or a large scale development, our pipeline scales to meet the needs of different sites and project phases. It involves:

1. Real-Time Hazard Detection : YOLOv11 tracks all movements on-site workers, machinery, and barriers alerting supervisors to potential risks like workers entering restricted zones or equipment misuse.‍
2. Precise Inspections with SAM : SAM segments and analyzes site images to detect cracks, structural issues, or instability that may be overlooked during manual inspections, ensuring accurate and frequent checks.‍
3. Adaptability to Site Changes : SAM’s zero-shot generalization adapts to evolving sites, detecting new objects or changes without needing retraining, keeping the safety system relevant as the project progresses.‍
4. Streamlined Data Collection and Insights : The pipeline collects and analyzes real-time data, providing actionable insights to improve safety protocols and decision-making.

By integrating this construction sites can significantly enhance safety, reduce risks, and ensure continuous, data-driven improvement in their operations.

Future Implications: What’s Next for AI in Construction Safety?

The future of construction safety is evolving toward proactive risk management, moving beyond detection to preventing accidents and protecting workers before they happen.

• Wearable Device Integration: Workers equipped with smart helmets or AR glasses synced to the system could trigger safety alerts and hazard detection in real time.
• Building a Digital Twin: Combining real-time data and sensors to create a virtual replica of the site, predicting hazards and intervening before accidents occur.

Interested in learning more?

In Part 2, we’ll dive into a hands-on demo with a code walkthrough and real-world examples.
Contact us to discover how Ionio can transform construction safety, streamline inspections, and reduce risks.