Machine-Learned Echo Imaging for Accurate Fish Counting
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Solution Overview
Problem
Existing methods struggle to accurately count the number of fish in underwater spaces, such as aquaculture pens, despite the use of fish finders and acoustic devices, which can determine fish distribution and density but not the exact count.
Innovation Solution
A fish count calculation method using machine learning to estimate the number of fish through constructing an estimator with training data sets of echo images from sound waves reflected by fish, allowing for the calculation of fish numbers in underwater spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fish finders using underwater ultrasonic waves are used to estimate fish amount and survey fish school resources, then the distribution, depth, and density of fish schools can be determined, but the number of fish cannot be counted
Solution Approach 1:
The patent replaces the conventional acoustic measurement system with a machine learning-based image analysis system. Instead of using fish finders that only provide density and distribution data, the invention uses cameras to capture images of fish and applies machine learning algorithms to accurately count individual fish, thereby obtaining precise fish number information that was previously unavailable
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between the captured images and the fish count result. The machine learning model processes the complex image data and extracts accurate fish counting information, serving as a mediator that transforms raw image data into meaningful quantitative data about fish numbers
2Area of stationary object
If cameras are used to monitor aquaculture pens, then visual observation is possible, but only a part of the pen can be observed and accurate fish counting remains difficult
Solution Approach 1:
The patent transitions from two-dimensional camera images to three-dimensional fish count estimation by incorporating machine learning algorithms that can process and analyze image data to infer the total fish population. The system uses trained models to estimate fish numbers based on partial visual observations, effectively extending the observable area coverage while maintaining counting accuracy
Solution Approach 2:
The patent changes the parameter being measured from direct visual counting to machine learning-based estimation. By training the machine learning model with labeled data, the system learns to accurately estimate fish counts from images, transforming the measurement approach from manual observation to automated intelligent analysis
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
Enables accurate and efficient counting of fish in underwater environments, enhancing monitoring and management of aquaculture operations.
Implementation Method 1
training echo image based on sound waves received upon being reflected by the fish when sound waves are transmitted in the underwater space
Data Source
AI summary
In the present invention, an estimator for estimating the number of fish present in an underwater space is constructed by means of machine learning using, as teaching data, a plurality of data sets for learning that each include an echo image for learning, the echo image being based on received sound waves reflected by fish when sound waves are transmitted in an underwater space where fish are present, and the number of fish present in the underwater space in the echo image. The plurality of data sets for learning each include: an echo image for learning that is based on received sound waves reflected by fish when sound waves are transmitted in the underwater space where fish are present; and the number of fish present in the underwater space in the echo image. The estimator is used on an echo image generated on the basis of received sound waves reflected by an unknown number of fish present in the underwater space after transmitting sound waves in the underwater space, so as to calculate the number of the unknown number of fish present in the underwater space.