Fish Behavior Simulation for Accurate Position Annotation
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Solution Overview
Problem
The challenge of accurately estimating information about a herd of fish, such as the number of fish, from captured images is hindered by the difficulty in creating large amounts of high-quality training data covering various conditions, and manual annotation of fish positions in overlapping regions is prone to inaccuracies.
Innovation Solution
An information processing apparatus generates simulation images of fish behavior in a three-dimensional virtual space using internal, external, and group parameters to automatically calculate and annotate fish positions, enabling the creation of high-quality training data for machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of fish positions in captured images is performed, then training data can be created, but the work is extremely time-consuming and prone to inaccuracies in overlapping regions
Solution Approach 1:
The patent uses simulation to create virtual copies of fish and their environments, generating synthetic training images that replicate real-world scenarios without requiring manual annotation of actual captured images. This copying approach eliminates time-consuming manual work while maintaining annotation accuracy through automated simulation processes.
Solution Approach 2:
The simulation system automatically generates annotated training data without human intervention. The simulation apparatus self-services by autonomously creating fish position annotations based on virtual fish behavior models, eliminating the need for manual annotation while ensuring consistency and accuracy across all training images.
2Reliability
If a large amount of training data covering various patterns is created manually, then machine learning model accuracy improves, but the complexity and time required increases significantly
Solution Approach 1:
Instead of manually creating diverse training scenarios, the system copies real-world fish behavior patterns into virtual simulations. By replicating various fish species, environments, and behaviors in the virtual space, the system generates comprehensive training data covering all necessary patterns without manual intervention, reducing complexity while maintaining reliability.
Solution Approach 2:
The simulation system varies multiple parameters simultaneously (fish species, environment conditions, lighting, camera angles, fish behavior patterns) to generate diverse training scenarios. This parameter-based approach efficiently creates comprehensive training data coverage without the complexity of manual scenario creation, allowing systematic exploration of various patterns.
3Productivity
If simulation images are used for training data, then manual annotation work is reduced, but the accuracy of fish behavior representation may be compromised
Solution Approach 1:
The simulation apparatus copies realistic fish behavior patterns, physical properties, and environmental interactions into the virtual space. By accurately replicating how fish move, interact, and respond to environmental factors in simulation, the system maintains behavioral accuracy while achieving high productivity in training data generation.
Solution Approach 2:
The patent replaces manual mechanical annotation processes with automated simulation-based generation. This substitution uses computational models to represent fish behavior physics and biology, achieving both high productivity through automation and high reliability through accurate physical modeling of fish movements and interactions.
Data Source
AI summary
An information processing apparatus according to the present application includes: an acquisition unit configured to acquire individual information indicating a characteristic of behavior of an individual belonging to a group of living organisms to be processed arranged in a virtual space, relative information indicating a characteristic of the behavior relative to the individuals of other individuals located around the individual belonging to the group of living organisms to be processed with respect to the individual, and environmental information indicating a characteristic of an environment around the individual belonging to the group of living organisms to be processed; and a determination unit configured to determine a behavior of an individual belonging to the group of living organisms to be processed on the basis of the individual information, the relative information, and the environmental information acquired by the acquisition unit.