Introducing "Foids": A Revolutionary Bio-Inspired Fish Simulation Platform for Computer Vision Training

Project Summary

In the pursuit of advancing computer vision in the field of aquaculture, we are excited to introduce "Foids" - our innovative bio-inspired fish simulation platform. "Foids" is uniquely designed to generate realistic synthetic datasets, marking a significant milestone as the first-of-its-kind synthetic dataset platform specifically for fish. This platform stands out by creating all 3D scenes through simulation, making it a groundbreaking tool for training computer vision algorithms.

Addressing the Dataset Challenge

A major obstacle in the development of deep learning-based computer vision is the creation of annotated datasets. Collecting high-quality video datasets with sufficient variation is already a challenging task. However, the process becomes even more arduous when it involves annotating large video datasets frame by frame. This difficulty is particularly pronounced in the context of fish datasets. Underwater camera setup is complex, and in a typical fish farm cage, the number of fish can range up to 30,000. Each of these fish requires annotation with labels like bounding boxes or silhouettes, a task that is not only time-consuming but also tedious when done manually for even just a few minutes of video.

Our Solution: Realistic Synthetic Dataset Generation

To overcome this challenge, we have developed "Foids", a realistic synthetic dataset generation platform. This platform leverages detailed biological and ecological knowledge from the aquaculture field. One of the key advantages of "Foids" is its ability to easily generate scene data complete with annotation labels, derived from 3D mesh geometry data and transformation matrices. This not only streamlines the dataset preparation process but also ensures a high level of accuracy and variability in the synthetic data.

Outcome: Automated Fish Counting System

Utilizing a portion of the synthetic dataset generated by "Foids", we have successfully developed an automated fish counting system. This system demonstrates counting accuracy comparable to human observation, drastically reducing the time required for data processing compared to manual methods. Additionally, it minimizes the physical stress and injuries typically sustained by fish during traditional counting processes.

Conclusion

Our "Foids" platform is a testament to the possibilities that emerge when technology meets biology. By addressing the critical need for efficient and accurate dataset preparation in aquaculture, "Foids" not only enhances the capabilities of computer vision algorithms but also contributes to more humane and sustainable fish farming practices. This project represents a significant step forward in the application of artificial intelligence in environmental and aquacultural management.

Introducing Deep Foids: An Enhanced Fish Simulation Platform for Adaptive Aquaculture Monitoring

Project Evolution

Building on the success of our original "Foids" platform, we are proud to unveil "Deep Foids" - an enhanced version that represents a significant leap in fish simulation for aquaculture monitoring. The development of Deep Foids was driven by a critical insight: the original Foids required manual tuning of hyperparameters whenever there were changes in environmental conditions or fish species. This realization led us to refine and advance our platform, culminating in Deep Foids, a more adaptive and efficient solution.

Deep Foids: Addressing the Need for Adaptability

Deep Foids retains all the innovative features of the original platform while introducing advanced adaptability to varying aquacultural environments and fish species. This adaptability is crucial in accurately simulating the diverse and dynamic conditions of aquaculture farms.

Enhanced Automation with Deep Learning

The core advancement in Deep Foids lies in its integration of deep learning techniques. These techniques enable the platform to automatically adjust its hyperparameters in response to changes in environmental conditions and fish species. This level of automation not only increases the accuracy and relevance of the simulations but also significantly reduces the need for manual intervention.

Benefits of Deep Foids

Automated Parameter Adjustment: Deep Foids dynamically adjusts its settings to cater to different species and environments, ensuring high-fidelity simulations without manual tuning.
Increased Accuracy and Efficiency: By automating the adaptation process, Deep Foids enhances the accuracy of fish behavior simulation and streamlines the dataset generation process.
Broader Application Scope: The ability to automatically adjust to varying conditions expands the usability of Deep Foids across a wider range of aquacultural environments and species.
Improved Training for Computer Vision Models: With more accurate and diverse datasets, computer vision models trained using Deep Foids are better equipped for real-world aquaculture monitoring tasks.

Conclusion

Deep Foids represents a pivotal development in our ongoing effort to optimize aquaculture monitoring through cutting-edge technology. By addressing the limitations of manual parameter tuning in the original Foids platform, Deep Foids stands as a testament to our commitment to continuous improvement and innovation. This advanced platform not only enhances the capabilities of computer vision in aquaculture but also paves the way for more sustainable and efficient fish farming practices.