Bionic Eagle-Eye Channel Flow Measurement With Optical Flow
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Solution Overview
Problem
Existing channel flow measurement methods suffer from inaccuracies due to deviations between actual and theoretical conditions, high construction and maintenance costs, and sensitivity to weather and water quality, leading to unreliable data.
Innovation Solution
A method and apparatus utilizing bionic eagle-eye vision to measure channel flow by acquiring a video, extracting image frames, calculating optical flow values, and using pre-trained models to determine average cross-section flow velocity and water level, incorporating image preprocessing and segmentation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structure-based flow measurement methods are used, then flow measurement can be achieved, but construction cost and maintenance cost are high
Solution Approach 1:
The patent replaces mechanical structure-based flow measurement systems with an optical vision system. Specifically, it uses a camera to capture water surface images and applies optical flow algorithms to calculate flow velocity, thereby eliminating the need for physical measurement structures like current meters, weirs, and sluice gates. This substitution dramatically reduces construction and maintenance costs while maintaining measurement functionality.
Solution Approach 2:
The patent creates a visual copy of the water flow by capturing images of the water surface and processing these images to extract flow information. Instead of directly measuring flow with physical instruments, the system copies the visual appearance of flowing water and analyzes it through image processing techniques, achieving flow measurement without physical intervention in the water channel.
2Device complexity
If existing non-contact flow measurement methods are used, then construction cost is reduced, but measurement accuracy is affected by weather, temperature, objects in rivers, and water quality
Solution Approach 1:
The patent focuses on measuring flow at the water surface layer rather than attempting to measure the entire water column. By selecting specific local regions of interest on the water surface and tracking optical flow in these regions, the system achieves accurate flow measurement while being less sensitive to environmental factors affecting the broader water body. This localized measurement approach improves robustness against weather and water quality variations.
Solution Approach 2:
The patent employs image processing techniques that adjust parameters such as contrast enhancement, noise filtering, and feature detection thresholds to maintain measurement accuracy under varying environmental conditions. By dynamically adjusting these parameters based on image quality and environmental factors, the system compensates for the effects of weather, temperature, and water quality on measurement precision.
3Loss of information
If optical flow calculation is performed on the entire water surface, then comprehensive flow data is obtained, but computational complexity and processing time increase
Solution Approach 1:
The patent divides the water surface into multiple regions of interest and selectively processes only those regions where flow features are most prominent or relevant. This segmentation approach allows the system to capture essential flow information from key areas while avoiding unnecessary computation in regions with minimal or irrelevant flow patterns, thereby reducing processing time while maintaining data completeness.
Solution Approach 2:
The patent applies partial action by focusing computational resources on processing a subset of the total water surface area that provides sufficient flow information. Rather than exhaustively analyzing every pixel across the entire water surface, the system identifies and processes representative regions that capture the dominant flow characteristics, achieving an optimal balance between data completeness and processing efficiency.
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 method provides high accuracy and simplicity in channel flow measurement by inferring global flow from local video data, reducing errors and enhancing measurement efficiency.
Implementation Method 1
calculating an optical flow value of the target observation image, and calculating a surface flow velocity value of the target observation image in a world coordinate system based on position change information of the optical flow value
Data Source
AI summary
A method for measuring channel flow based on bionic eagle-eye vision and an apparatus thereof are provided. The method includes: acquiring a channel flowing video, and carrying out image frame interval extraction on the channel flowing video to obtain a target observation image; calculating an optical flow value of the target observation image, and calculating a surface flow velocity value of the target observation image in a world coordinate system based on position change information of the optical flow value of the target observation image between image frames and a spatial resolution of the target observation image; acquiring an average cross-section flow velocity based on a pre-trained surface cross-section flow velocity coupling model, and calculating the channel flow based on the average cross-section flow velocity and a cross-section water level value.