AI Thermal Worker Detection Near Hazardous Machinery
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Solution Overview
Problem
Existing human body detection technologies in factories struggle to accurately and rapidly detect workers approaching hazardous areas, especially when they are obscured by objects or in close proximity to high-temperature objects.
Innovation Solution
An apparatus and method utilizing thermal imaging combined with deep learning-based artificial intelligence to detect workers. This system includes an image receiver, a worker detector, a hazard detector, and a hazard controller, which can identify workers in thermal images, determine if they have entered hazardous areas, and stop machinery operations as necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If thermal imaging is used to detect workers, then detection capability in obscured or high-temperature environments is improved, but device complexity increases
Solution Approach 1:
The system segments the detection task into multiple specialized modules: thermal image receiver for heat signature capture, worker detector for identifying human figures, hazardous area detector for defining danger zones, and hazard controller for safety management. Each module handles a specific aspect of the detection process, improving overall reliability while making the complex system more manageable through functional decomposition
Solution Approach 2:
The patent introduces an artificial intelligence model as an intermediary between the thermal imaging data and the detection decisions. This AI intermediary processes the complex thermal data, extracts relevant features, and makes detection judgments, thereby improving detection capability while abstracting the complexity from the core detection logic
2Measurement precision
If deep learning-based AI model is used for worker detection, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-defining hazardous areas and pre-training the AI model with extensive worker and environment data before actual detection. This preparation work is done in advance, allowing the detection process itself to be faster and more accurate without requiring complex real-time computations for area definition or model adaptation
Solution Approach 2:
The AI model focuses on detecting only the most critical features relevant to worker identification and hazardous area detection, rather than processing all possible image features. This selective approach achieves sufficient detection accuracy while reducing processing time by avoiding unnecessary computational steps
3Object-affected harmful factors
If the system stops machinery operation upon hazard detection, then worker safety is improved, but productivity decreases
Solution Approach 1:
The system implements a feedback mechanism where the hazard controller continuously monitors detection results and adjusts machinery operation accordingly. When hazards are detected, the system provides feedback to stop or alert, and when areas are clear, normal operation resumes. This closed-loop control ensures worker safety while minimizing unnecessary interruptions to productivity
Solution Approach 2:
The system takes preliminary anti-action by proactively identifying and marking hazardous areas before workers can enter them. The hazardous area detector pre-defines danger zones based on thermal signatures of high-temperature objects, and the system prepares stop commands in advance, preventing accidents before they occur rather than reacting after hazards are encountered
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution effectively detects workers in proximity to hazardous areas, even when obscured, and promptly stops machinery to prevent accidents, thereby enhancing worker safety in industrial environments.
Implementation Method 1
an image receiver configured to receive a thermal image from a thermal imaging camera
Data Source
AI summary
In an embodiment, an apparatus may include an image receiver configured to receive a thermal image from a thermal imaging camera, a worker detector configured to detect a worker from the received thermal image by using an artificial intelligence model, a hazard detector configured to detect a hazard based on whether the detected worker has entered a preset hazardous area in the thermal image, and a hazard controller configured to, in response to the hazard being detected, send a hazard notification and stop operation of a work machine in the hazardous area, where a position of the detected worker can be estimated in pixel units.