Facility Surveillance Using Multi-Modal Sensors for Critical State Detection
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Solution Overview
Problem
Existing surveillance systems struggle to accurately detect critical states and anomalies in facilities due to limitations in sensor modalities, environmental conditions, and the need for extensive on-site training, leading to high false-positive and false-negative rates.
Innovation Solution
A system that combines multiple surveillance sensors operating in different modalities, such as RGB, depth, and thermal imagery, using machine learning to adapt to specific environments and contexts, enabling robust detection of critical states and anomalies by learning optimal sensor combinations and classifications based on contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple surveillance sensors operating in different modalities are combined, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple surveillance sensors operating in different modalities (RGB, depth, thermal) into a unified surveillance system. This merging of sensors allows the system to capture complementary information from different spectral and spatial domains, thereby improving detection accuracy for critical states and anomalies while managing the complexity through integrated processing.
Solution Approach 2:
The surveillance system is designed with multi-functional capability by incorporating sensors that operate in different modalities. Each sensor type serves multiple purposes: RGB cameras capture visual appearance, depth sensors measure spatial relationships, and thermal cameras detect temperature variations. This universality allows a single system to perform diverse surveillance functions simultaneously, improving detection accuracy without proportionally increasing complexity.
2Reliability
If machine learning is used to adapt to specific environments, then false-positive rate is reduced, but loss of time for training increases
Solution Approach 1:
The system performs preliminary machine learning training during system deployment and setup phases before actual surveillance operations begin. By conducting this training in advance, the system learns optimal parameters and characteristics of the specific facility environment, enabling it to distinguish between normal and anomalous states more accurately during operational use, thereby reducing false positives without impacting operational time.
Solution Approach 2:
The system incorporates feedback mechanisms that allow continuous refinement of machine learning models based on operational data. By analyzing actual surveillance data and outcomes, the system can iteratively improve its detection algorithms and adapt to environmental variations, reducing false-positive rates over time while minimizing the need for extensive retraining during operational phases.
3Measurement precision
If extensive on-site training is conducted, then detection accuracy is improved, but productivity during deployment is reduced
Solution Approach 1:
The system implements a phased training approach where essential detection capabilities are established through concise initial training, and additional refinement occurs progressively. This partial action approach provides sufficient detection accuracy for critical applications while significantly reducing the time required for complete system deployment, allowing faster productivity during the deployment phase.
Solution Approach 2:
The system employs parameter optimization techniques that allow detection accuracy to be adjusted by modifying key parameters rather than conducting extensive training. By changing parameters such as detection thresholds, sensitivity levels, and algorithm weights, the system can achieve high detection accuracy more efficiently, thereby improving deployment productivity without sacrificing measurement precision.
Data Source
AI summary
Systems and methods for surveillance of a facility including facility elements. The system includes a central computing unit providing a digital model of the facility providing topological or logical or functional relationships of the facility elements, surveillance sensors adapted for surveillance of a plurality of the facility elements and for generation of surveillance data, communication means for transmitting data from the surveillance sensors to the central computing unit, and state derivation means configured to analyse the surveillance data and derive a state of a respective facility element. The central computing unit is configured to record a state pattern by combining states of at least one facility element based on at least one relationship of the facility element provided by the facility model, provide a state pattern critical-noncritical classification model which considers relationships provided by the facility model, and perform a criticality-classification based on the relationship.