Image Recognition & Object Detection for The Nature Conservancy
The Nature Conservancy is one of the world’s leading conservation organisations, working in more than 70 countries to protect ecologically important lands and waters for nature and people. In the Western and Central Pacific, where 60% of the world’s tuna is caught, illegal, unreported, and unregulated fishing is threatening marine ecosystems, and global seafood supplies. The Nature Conservancy wanted to develop a camera-based solution for automatic detection and classification of fish species caught by the commercial fishing fleet. To find the solution they summoned the global tech community to participate in a Fisheries Monitoring contest at Kaggle.
Ciklum R&D Engineering team received the training dataset of about 3,700 photos. The team needed to build an algorithm to identify a certain fish type. Traditional algorithms would not be effective enough for the required solution, hence Ciklum engineers used innovative object detection methods based on convolutional neural networks.
Ciklum R&D team developed a series of algorithms to automatically detect and classify different species of tuna, sharks and more that fishing boats catch, to accelerate the video review process. Fish detection algorithm – localization precision is 95%, recall – 80% Classification algorithm for different species of fish on a boat with 93% accuracy