Emergency Image Hazard Recognition Using CNN and Relational Reasoning
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Solution Overview
Problem
Images received during emergency incidents, such as those sent to PSAPs, may be of poor quality or contain indistinguishable objects, missing critical information on hidden dangers that could complicate emergency response.
Innovation Solution
A computer-implemented method using Convolutional Neural Networks (CNN) and Relational Networks (RN) with deep learning algorithms to identify and classify objects in emergency images, correlating them to recognize secondary hazardous situations and providing alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning algorithms (CNN and RN) are applied to analyze emergency images, then identification accuracy of hidden dangers is improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary image processing and object detection using CNN before relational reasoning with RN. By pre-identifying objects and their features in the image, the system reduces the computational burden during the relational reasoning phase, enabling faster processing while maintaining high accuracy in identifying hidden dangers.
Solution Approach 2:
The analysis process is divided into two distinct stages: first, CNN performs object detection and feature extraction; second, RN performs relational reasoning based on the extracted features. This segmentation allows each algorithm to specialize in its strength while reducing overall processing time compared to using a single complex model.
2Reliability
If deep learning algorithms are applied to identify all objects in emergency images, then completeness of hazard identification is improved, but device complexity increases
Solution Approach 1:
The system segments the complex task of hazard identification into two specialized modules: CNN for object detection and RN for relational reasoning. This division reduces the complexity of individual components while maintaining comprehensive hazard identification through their coordinated operation.
Solution Approach 2:
The CNN acts as an intermediary that extracts object features and feeds them to the RN for relational reasoning. This intermediary layer simplifies the input for the RN by providing pre-processed object information, reducing the overall system complexity while ensuring complete hazard identification.
3Loss of information
If image processing and deep learning analysis are performed on emergency images, then information completeness is improved, but processing complexity increases
Solution Approach 1:
The processing pipeline is segmented into distinct stages: image processing, CNN-based object detection, and RN-based relational reasoning. Each stage handles a specific aspect of information extraction, reducing processing complexity while ensuring comprehensive information completeness through the cumulative effect of all stages.
Solution Approach 2:
Image processing and object detection are performed as preliminary actions before relational reasoning. This preliminary extraction of object information simplifies the subsequent relational analysis by the RN, reducing processing complexity while maintaining complete information about the emergency scene.
Data Source
AI summary
A computer-implemented method of handling an emergency incident can include receiving information on an emergency incident that includes at least one image of the emergency incident, applying a Convolutional Neural Network (CNN) object recognition and classification process for identifying and marking objects on the at least one image that are related to the emergency incident and that may cause at least one secondary hazardous situation, processing the data relating to the identified and marked objects by applying a deep learning algorithm to the data in a Relational Network (RN) architecture, wherein the image on the basis of the identified and marked objects is correlated to a set of recognized objects in a database for classifying the emergency. A communication network, communication apparatus, and an emergency processing unit are also provided. Embodiments of such machines and systems can be configured to implement embodiments of the method.