Computer-Vision Safety Detection for Real-Time Unsafe Behavior
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing safety training programs in manufacturing are insufficient to prevent workplace injuries and deaths, as they rely heavily on human compliance and are prone to complacency, leading to frequent safety violations and incidents.
Innovation Solution
A computer-vision based Safety Detection System (SDS) using spatial and temporal classifiers to identify unsafe practices in real-time, providing feedback to workers through decision support tools, and integrating multispectral imaging for accurate detection of safety violations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual safety monitoring and training programs are used, then implementation cost is low, but safety effectiveness is insufficient leading to frequent injuries and deaths
Solution Approach 1:
The patent replaces manual safety monitoring (mechanical/human-based) with an automated computer vision system using sensors, processors, and machine learning models. This substitution enables continuous automated detection of unsafe behaviors and conditions, significantly improving safety effectiveness while reducing reliance on human compliance and manual intervention.
Solution Approach 2:
The safety monitoring system performs self-service by automatically detecting unsafe conditions, analyzing sensor data, and generating alerts without requiring constant human supervision. The machine learning models continuously learn from data and improve detection accuracy autonomously, enabling the system to maintain and enhance its own performance over time.
2Measurement precision
If continuous safety monitoring is implemented, then safety detection capability is improved, but false positives increase reducing system reliability
Solution Approach 1:
The system performs preliminary action by training machine learning models on extensive safety data before deployment. The models are pre-trained to recognize patterns of unsafe behaviors and conditions, enabling them to make accurate predictions in real-time. This preliminary training reduces false positives by establishing robust detection criteria before actual safety monitoring begins.
Solution Approach 2:
The safety monitoring system implements feedback loops where detection results are continuously analyzed and used to refine detection algorithms. False positives are identified and fed back into the training process, allowing the machine learning models to learn from errors and improve accuracy over time. This feedback mechanism progressively reduces false positive rates while maintaining high detection precision.
3Loss of information
If comprehensive safety training programs are used, then worker knowledge is improved, but compliance remains insufficient due to human factors
Solution Approach 1:
The patent replaces human-based safety compliance (prone to forgetfulness and inconsistency) with automated computer vision monitoring. The system continuously observes and detects unsafe behaviors objectively, eliminating the need for workers to consciously remember and apply safety protocols. This substitution ensures consistent enforcement of safety rules regardless of worker knowledge or compliance motivation.
4Object-affected harmful factors
If traditional safety barriers and interlocks are used, then basic protection is provided, but they cannot detect hazards like loose clothing or long hair
Solution Approach 1:
The patent implements a universal safety monitoring system that can detect multiple types of hazards simultaneously using the same sensor array and machine learning models. The system identifies unsafe behaviors, improper PPE usage, hazardous conditions, and personal hazards (like loose clothing or long hair) through comprehensive image analysis. This multi-functional approach replaces multiple specialized detection devices with a single integrated system.
Data Source
AI summary
An example provides a method, including: obtaining, using one or more sensor systems, sensor data for one or more persons in an environment comprising machinery; analyzing, using a processor, the sensor data using a trained model to identify the one or more persons and an associated time series of actions; determining, using the associated time series of actions, if the one or more persons are engaging in behavior indicative of an unsafe practice; and thereafter presenting safety information to a visual display system, connected software system or other decision support tool.