AI Can Dive – SingTown Technology Makes Marine Life Recognition Smarter

In marine ecological conservation and scientific research monitoring, the real-time identification of various underwater organisms (such as starfish, sea urchins, corals, etc.) is crucial for studying biodiversity and changes in water quality environments.

Traditional manual diving observations are constrained by environmental factors, exhibiting low efficiency and insufficient accuracy.

To this end, the marine ecological monitoring enterprise has introduced SingTown's OpenMV intelligent camera recognition technology, utilising AI machine vision and image recognition algorithms to achieve automatic underwater multi-target identification and classification.

This system can be applied in scenarios such as marine life census, habitat monitoring, and ecological anomaly detection, significantly enhancing the efficiency and accuracy of marine ecological data acquisition.

Marine Ecological Monitoring Enterprise utilises SingTown Technology's OpenMV intelligent camera recognition technology to achieve automatic identification and classification labelling of various marine organisms.

The system deploys visual monitoring modules underwater, utilising high-definition cameras to capture real-time seabed images and identify various aquatic organisms such as starfish, sea urchins, and shellfish.

The system utilises a deep convolutional neural network (CNN) algorithm, trained on thousands of labelled samples, enabling it to distinguish objects based on their morphology, colour, and edge texture. Even in complex scenarios such as underwater lighting variations, bubble disturbances, or minor drifts, the system maintains stable recognition performance.

During operation, the camera utilises target detection, feature matching, and region classification algorithms to automatically generate coloured rectangular boxes for each target in the frame, annotating them with category names and confidence levels (e.g., starfish 0.83, echinus 0.88).

The detection results are computed in real-time by the edge AI chip of the OpenMV smart camera, which can independently perform recognition, annotation, and data encapsulation underwater. The results are then output to the ground monitoring terminal via serial port or wireless network for ecological statistics or pollution analysis.

This solution achieves automatic detection, classification, counting, and visual annotation of multiple biological targets, providing an efficient, reliable, and locally deployable machine vision solution for marine ecological protection, scientific research surveys, and underwater environmental assessment.

Through SingTown Technology's OpenMV intelligent camera recognition technology, marine ecological monitoring enterprises have achieved an intelligent upgrade from manual observation to AI visual recognition and data-driven analysis.