Smarter Maritime Surveillance with Illegal Fishing Detection on Matrice
Illegal fishing detection is a critical challenge for maritime security and environmental conservation, impacting sustainable fisheries and marine ecosystems. With AI-powered computer vision, unauthorized fishing activities can be identified and localized in real-time, enabling faster response times and better enforcement of regulations.
This blog details how Matrice enables no-code AI deployment for illegal fishing detection using deep learning, covering:
Dataset Preparation
Dataset Annotation
Model Training
Model Evaluation
Model Inference
Model Deployment
Dataset Preparation
The dataset for this project includes 3,930 annotated images of maritime scenes, capturing various fishing vessels and activities, labeled to distinguish legal from illegal fishing practices. The dataset is divided to support robust training and validation:
Total Samples: 3,930
Training Set: 2,752
Validation Set: 787
Testing Set: 391
Model Training
We utilized a YOLO-based object detection model to identify and classify fishing activities in images. YOLOv8 was chosen for its high accuracy and real-time processing capabilities, ideal for detecting subtle differences in maritime imagery.
Model: YOLOv8 (multi-class object detection)
Batch Size: 16
Epochs: 50
Learning Rate: 0.001
Optimizer: Auto
Momentum: 0.95
Weight Decay: 0.0005
Performance Metrics
The trained model was evaluated on validation and test datasets, using metrics like precision and recall to assess its effectiveness in detecting illegal fishing activities.
Metric |
Value (Test) |
Value (Val) |
|---|---|---|
Map@50 |
0.72 |
0.684 |
Precision |
0.70 |
0.71 |
Model Inference
The trained model supports export to multiple formats, enabling deployment across various platforms, from coastal monitoring systems to cloud-based analytics.
Supported formats include:
PyTorch (.pt)
ONNX
TensorRT
OpenVINO
This flexibility ensures compatibility with diverse deployment scenarios, such as onboard vessel systems, satellite imagery analysis, or coastal surveillance stations.
Model Deployment
Using Matrice, the trained model can be deployed seamlessly via a no-code interface. Matrice supports:
Real-time inference
API-based integration
Visual dashboards for monitoring
Applications include:
Automated detection of illegal fishing in protected marine areas
Real-time alerts for maritime authorities
Environmental monitoring and compliance tracking
Conclusion
AI-powered illegal fishing detection enhances maritime surveillance and supports sustainable fisheries management. With Matrice, deploying such solutions is straightforward, enabling rapid implementation and reduced operational costs. By leveraging YOLOv8 and a well-curated dataset, you can build high-performing models to protect marine ecosystems with advanced visual intelligence.
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