How Artificial Intelligence Can Revolutionize the Fishing Industry
Introduction
The fishing industry is undergoing a quiet but powerful transformation. With advancements in artificial intelligence (AI), computer vision, and machine learning, it is now possible to monitor, count, and classify fish directly on fishing vessels—without manual intervention. One particularly promising approach uses CCTV cameras mounted on ships to track catch data in real time.
During my holiday break, I came across a fascinating discussion on the Kaggle platform about using AI models to detect fish species and measure catch quantities. This inspired me to share my own experience with a similar project I worked on back in 2018.
The Project: AI-Powered Fish Counting
In 2018, I embarked on a challenge to build a low-cost AI system that could:
- Count fish automatically
- Classify species accurately
- Operate without pre-labeled training data
The Setup
We used CCTV footage recorded on a commercial fishing boat. The cameras continuously captured the flow of fish as they were brought onboard. This footage served as our raw dataset.
The Technology Stack
- Data Platform: Kaggle (for data hosting, model training, and collaboration)
- Machine Learning Framework: PyTorch
- Model Type: Convolutional Neural Networks (CNNs) for image classification
- Training Approach: Semi-supervised learning to work with minimal labeled data
How It Worked
- Video Processing – Raw CCTV footage was split into frames for image analysis.
- Object Detection – Neural networks identified fish in each frame using bounding box detection.
- Classification – Models were trained to distinguish between species based on shape, color, and pattern.
- Counting Algorithm – A tracking algorithm ensured that the same fish was not counted multiple times.
- Model Optimization – Hyperparameter tuning improved accuracy and reduced false positives.
The result? Our AI system could accurately count and categorize fish using nothing more than visual data from onboard cameras.
Why This Matters
This technology has huge potential for:
- Sustainable fishing – Ensuring catch limits are respected.
- Regulatory compliance – Providing accurate and verifiable catch records.
- Operational efficiency – Reducing the need for manual fish counting.
By automating the process, fishing companies can save time, reduce human error, and protect marine ecosystems.
Conclusion
Artificial intelligence is not just for big tech—industries like fishing can also benefit greatly from smart, low-cost solutions. My fish-counting project proved that with the right tools and algorithms, even traditional sectors can embrace the future.
If you’re curious about AI applications in fishing or want to discuss similar projects, feel free to reach out.
Portfolio: baronsa.dev
