Forest Fire Detection System with Direction Estimation
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Solution Overview
Problem
Current forest fire detection systems face challenges in early detection and efficient notification, often resulting in large-scale damage due to delayed identification and redundant notifications.
Innovation Solution
A forest fire detection system incorporating an artificial intelligence-based module for image analysis, a direction estimation module to determine the fire's direction on a map, and a same event determination module to assess whether multiple images represent the same event, thereby reducing unnecessary notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple forest fire detection images are analyzed to improve detection accuracy, then the reliability of fire detection is improved, but the system generates redundant notifications and increases processing time
Solution Approach 1:
The system performs preliminary actions by estimating the direction of the forest fire from detection images and storing this directional information before notification is needed. This preliminary direction estimation enables rapid comparison with subsequent detections, allowing the system to quickly determine if a new detection represents a different event requiring notification, thus resolving the contradiction between thorough analysis and rapid response.
2Loss of information
If the system notifies users of every detected forest fire event, then the completeness of information is improved, but redundant notifications are generated causing information overload
Solution Approach 1:
The system implements feedback by comparing the direction of newly detected forest fires with previously detected and stored fire directions. This feedback mechanism enables the system to intelligently determine whether a new detection represents a distinct event requiring notification or a redundant detection of the same event, thus maintaining information completeness while eliminating notification redundancy through comparative analysis.
3Measurement precision
If the system processes all detection images in detail to ensure accurate event identification, then the measurement precision is improved, but the device complexity increases
Solution Approach 1:
The system extracts and utilizes only the critical directional information from detection images rather than performing detailed analysis of entire image content. By extracting just the fire direction as a key parameter and comparing this simplified metric, the system maintains accurate event identification while significantly reducing processing complexity through selective information extraction.
Data Source
AI summary
Provided are a forest fire detection system and method capable of determining whether events. The forest fire detection system includes an artificial intelligence-based forest fire detection module configured to detect a forest fire from a captured image using an artificial intelligence model; a monitoring camera configured to monitor a predetermined area; a direction estimation module configured to estimate a direction of the forest fire on a map, using a plurality of forest fire detection images provided from the artificial intelligence-based forest fire detection module and data of the monitoring camera; and a same event determination module configured to determine whether events represented by the plurality of forest fire detection images are the same, based on the estimated direction of the forest fire.