Advanced Underwater Object Detection and Classification System for Aquatic Environment Monitoring

The organization faces difficulties achieving real-time, high-accuracy detection and classification of plankton and other aquatic entities due to hardware constraints and outdated ML algorithms, leading to limited detection speed and suboptimal data quality for ecosystem analysis.

A research-focused organization specializing in underwater measurement and observation systems aimed at understanding aquatic ecosystems and supporting scientific and industrial decision-making.

• Develop a real-time underwater object detection and classification system compatible with existing embedded hardware platforms.
• Increase detection and classification accuracy of plankton in ocean water samples.
• Ensure system performance aligns with hardware capabilities, matching camera frame rates without bottlenecks.
• Leverage modern machine learning algorithms and optimized architectures to speed up processing and improve results.
• Enable efficient data handling by utilizing RAM-based processing and multithreading approaches.
• Create a scalable solution adaptable to similar environmental monitoring tasks.

• Object detection module using YOLO-based architecture for identifying aquatic entities within video frames.
• Feature encoding using a modern CNN (e.g., EfficientNet) to generate vector representations of detected objects.
• Similarity comparison component employing a fast nearest neighbor search algorithm (e.g., Faiss) to match detected objects against reference datasets.
• Real-time frame acquisition from underwater cameras via dedicated libraries (e.g., PyCapture).
• RAM-based data processing to minimize I/O delays and accelerate performance.
• Multithreaded architecture separating detection, feature encoding, and comparison processes.
• Support for training detection models (e.g., YOLO with Darknet) and classification models (e.g., PyTorch-based EfficientNet).
• Data export functionality for storing detected object images and associated statistics.

• Underwater camera systems via PyCapture library or equivalent for video stream acquisition
• Embedded hardware platforms with GPU capabilities (e.g., NVIDIA Jetson modules)
• Data storage solutions for final image and statistics output

• Achieve processing speeds matching camera frame rate (up to 30 FPS)
• Ensure detection and classification accuracy surpass previous benchmarks to support ecosystem analysis
• System stability and robustness for continuous operation in harsh underwater environments
• Modular architecture facilitating maintenance and future scalability
• Data security during remote operations and data transfer

This project aims to deliver a high-precision, real-time underwater monitoring system capable of classifying plankton with increased accuracy and speed, improving data quality and supporting scientific ecosystems research. Anticipated improvements include processing speeds aligned with camera frame rates, enhanced detection accuracy, and more reliable ecosystem insights, thereby advancing oceanographic data collection and environmental decision-making.