Real-time detection and classification of objects in flowing water

This paper describes the design of a PC-based real-time machine vision system for detecting and classifying small marine organisms like fish eggs and planktons in flowing water. The system is called the Real-time FLow Imaging and Classification System, or ReFLICS for short, and it will automate the task of visually counting and classifying fish egg samples which is currently performed by trained humans. ReFLICS uses a line-scan image sensor to eliminate double counting and boundary effects. Using a combination of flowmeter and image-based flow error correction algorithm, ReFLICS's line-scan camera can work with changing flow. Design of the complete system from the camera and illumination housing to the machine vision software allows ReFLICS to work in the harsh environments of a ship at sea. Using an industry-standard multi-processor PC with PCI card pipeline image processor and running Microsoft Windows NT, ReFLICS can achieve the high performance required meanwhile maintaining relative low equipment, development, and maintenance costs. This paper provides the ReFLICS system design and presents initial results of the system.