AI Video Monitoring for Construction Equipment Collision Prediction
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Solution Overview
Problem
Construction sites face significant safety challenges due to the manual nature of current safety monitoring systems, which are prone to human error and do not provide continuous visibility, leading to accidents, injuries, and substantial economic losses.
Innovation Solution
A video monitoring system utilizing stereoscopic and 360-degree cameras, edge devices, and machine learning models for automated event detection and alerting, including collision prediction and direct vehicle actuation, to enhance safety and minimize accidents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual safety monitoring processes are used, then system simplicity is maintained, but safety reliability deteriorates due to human error and lack of continuous visibility
Solution Approach 1:
The system enables self-service through automated AI-powered monitoring that independently detects hazards, generates alerts, and notifies stakeholders without requiring continuous human intervention. The machine learning models automatically process video feeds and identify safety risks, making the system self-sufficient in maintaining safety monitoring.
Solution Approach 2:
Manual mechanical monitoring processes are replaced with automated electronic and software-based systems. Video cameras capture footage, which is then processed by machine learning algorithms to detect safety hazards, replacing the need for human operators to physically monitor construction sites.
2Measurement precision
If automated AI-powered video monitoring is implemented, then safety detection accuracy improves, but system cost increases due to advanced technology requirements
Solution Approach 1:
The system segments the monitoring function into modular components: video capture modules, machine learning processing modules, alert generation modules, and notification modules. This segmentation allows for targeted implementation and reduces overall implementation complexity by enabling phased deployment of specific functional components.
Solution Approach 2:
The machine learning models are designed to perform multiple safety detection functions using the same video input infrastructure. A single system can detect various hazards including unauthorized personnel, equipment anomalies, and environmental risks, providing multi-functionality that reduces the need for separate specialized systems.
3Reliability
If continuous human monitoring is deployed, then real-time safety oversight is achieved, but productivity decreases due to resource allocation requirements
Solution Approach 1:
The automated system performs continuous safety monitoring independently without requiring human resources to be allocated for this function. The machine learning models process video feeds continuously and generate alerts autonomously, freeing human workers to focus on construction tasks rather than safety monitoring duties.
Solution Approach 2:
The system provides real-time feedback through automated alerts and notifications when safety hazards are detected. This immediate feedback mechanism maintains continuous safety oversight while enabling rapid response to hazards without requiring constant human presence or intervention.
Data Source
AI summary
In many embodiments of the invention, a video monitoring system for construction sites includes one or more stereoscopic cameras configured to capture image data from multiple viewpoints over time, one or more 360-degree cameras configured to capture 360-degree image data, an edge device configured to receive the image data from the stereoscopic cameras and the 360-degree cameras, generate three-dimensional point clouds from the image data, recognize fiducial markers within the image data, identify objects and estimate movement of the objects in the point clouds using a plurality of machine learning models, and generate alerts based on the identified movement of the objects, and one or more client devices configured to receive the alerts from the edge device.