Image Recognition Size Estimation Using Reference Scale
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Solution Overview
Problem
Current image recognition technologies, despite integration with deep learning and road surveillance systems, struggle to accurately assess flood conditions beyond roads and lack methods for estimating the area of natural disasters, relying heavily on public notifications and failing to provide comprehensive monitoring of flood conditions.
Innovation Solution
A method for size estimation by image recognition using a given scale, where a reference object and target object are identified in an extracted image, allowing for the deduction of a corresponding scale to estimate the real size of the target object, improving accuracy through image extraction, data reading, and operational device processing, including space conversion and object enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If image recognition technology is used to monitor flood conditions, then the monitoring coverage can be expanded, but the accuracy of size estimation remains insufficient without a reference scale
Solution Approach 1:
A reference object with known size is introduced as an intermediary element in the flood scene to establish a scale relationship. The reference object serves as a mediator between the image data and real-world dimensions, enabling accurate size estimation of flood-affected areas by comparing target objects and regions against the known reference scale.
2Extent of automation
If deep learning technology is integrated with road surveillance systems, then automatic image recognition can be achieved, but the system cannot assess flood regions beyond roads
Solution Approach 1:
The image recognition system is enhanced with multi-functionality to handle both road-specific flood monitoring and general area assessment. By incorporating reference object detection and scale deduction capabilities, the system can now process diverse targets including but not limited to road floods, enabling universal application across different disaster scenarios and geographical areas.
3Quantity of substance
If multiple institutions provide monitoring images, then the data quantity increases, but manual monitoring becomes difficult
Solution Approach 1:
The system implements self-service automation where the image recognition algorithm automatically processes, analyzes, and extracts information from multiple monitoring images without human intervention. The automated scale deduction and size estimation functions enable the system to independently handle large volumes of images from multiple institutions, eliminating the need for manual monitoring.
4Measurement precision
If reference objects are identified and scale is deduced, then the size estimation accuracy improves, but the device complexity increases
Solution Approach 1:
Reference objects with known sizes are pre-identified and registered in the system database before actual flood monitoring occurs. The scale deduction algorithms are pre-configured with reference object characteristics, enabling rapid and accurate size estimation during disaster events without requiring complex real-time calculations or additional hardware components.
Data Source
AI summary
The present invention relates to a method for size estimation by image recognition of a specific target using a given scale. First, a reference objected is recognized in an image and the corresponding scale is established. Then the specific target is searched and the size of the specific target is estimated according to the acquired scale.