Monitoring Progress and Safety Risks with Computer Vision

The Problem

Managing safety and quality across large construction sites is a complex operational challenge. Project managers must monitor worker safety practices, stick to timelines, and identify potential issues before they cause delays or hazards. Traditionally, these responsibilities rely heavily on manual site inspections and periodic reporting from field teams.

A commercial construction company faced these challenges across several active projects. Multiple crews were working simultaneously across large sites in areas that were difficult to monitor continuously. While site supervisors and safety officers conducted regular inspections, it was difficult to maintain consistent visibility across the entire construction environment.

The organization installed fixed cameras around construction sites and occasionally used drones to capture aerial imagery. These tools generated valuable visual data, but the images were largely reviewed manually (or often ignored). With thousands of images captured each week, monitoring progress and safety was infeasible.

In addition, field engineers frequently captured photos using mobile phones during routine inspections. These images documented site conditions and potential problems but were often in separate systems or shared informally through messaging apps. The organization lacked a consistent way to analyze images and video.

The company needed a system that could continuously monitor construction activity, identify potential safety risks, and help project managers track progress without relying entirely on manual inspection.

QSC’s Modeling Approach

QSC developed a computer vision system designed to analyze images captured throughout the construction workflow.

The system integrates imagery from several sources, including fixed site cameras, drone surveys, and photographs collected by mobile devices. These images are routed through a centralized processing pipeline where they are analyzed consistently and stored properly.

Using these image datasets, QSC developed computer vision models capable of detecting key objects and activities relevant to construction operations. Deep learning techniques were used to train models that can identify workers, equipment, safety gear, and construction materials within images.

Object detection models allow the system to recognize conditions such as workers entering restricted areas, missing safety equipment, or unusual equipment behavior. Anomaly detection models identify unexpected visual patterns that might indicate emerging issues on the construction site.

By analyzing images from fixed cameras and drone flights, the system can also monitor visible changes in construction progress over time. This helps project managers track whether construction activities are progressing as expected and identify areas that require attention.

In addition to the modeling components, QSC developed software tools to make the system usable for project teams.

A desktop application provides project managers with a centralized dashboard where they monitor site activity, review flagged images, and explore historical visual data from multiple projects. This allows teams to investigate potential issues and compare conditions across different areas of a site.

Decision Support in Practice

The system provides construction teams with a continuous view of site activity and safety conditions.

Images captured by fixed cameras and drones are analyzed automatically as they enter the system. When potential safety risks or unusual conditions are detected, the system flags the relevant images, highlights them in the monitoring dashboard, and sends alerts to managers.

The software also helps teams track construction progress by identifying changes in materials, equipment placement, and structural development across different areas of the site. Managers verify that the project is on schedule and use the information to improve timeline planning for future projects.

Field engineers and safety officers use the mobile application to capture additional images during inspections. These images provided context for flagged events and help expand the dataset used to train the computer vision models.

By combining automated image analysis with on-site observations, the system allows construction teams to monitor safety conditions and project progress more consistently across large and complex worksites.

Outcome

The organization gained the ability to monitor processes across construction sites. They have a better understanding of bottlenecks and safety concerns.

Instead of relying entirely on periodic inspections, the company has a system that continuously analyzes visual data from cameras, drones, and field observations. Potential safety risks are identified earlier, and project managers investigate issues before they escalate into larger problems.

The integrated software platform improves coordination between project managers, safety officers, and field engineers. Images captured across the site are available in a single system, making it easier to review conditions, investigate incidents, and track changes over time.

Equally important, the mobile application allows crew members to contribute directly to the monitoring system by capturing images during inspections. These observations strengthen the system’s ability to recognize new conditions and provide valuable context for construction managers.

For the organization, the result is not simply a new inspection tool. It is a unified visual monitoring system that helps teams maintain safer construction sites while improving visibility into project progress.