An Automated Fish Species Classification and Migration Monitoring System

Abstract

The quantification of abundance, distribution, and movement of fish is critical to ecological and environmental studies of fish communities. To properly manage, regulate, and protect migratory fisheries it is essential to accurately monitor numbers, size, and species of fish at specific fish passages during migratory seasons. Currently, all monitoring is done manually with significant time and financial constraints. An automated fish classification system will simplify data gathering and improve data accuracy. In this research, 22 images of 9 target species were recorded. The contour of each image was extracted to form a closed curve for shape analysis. A new shape analysis algorithm was developed for removing edge noise and redundant data points such as short straight lines. A curvature function analysis was used to locate critical landmark points. The fish contour segments of interest were then extracted based on these landmark points for species classification. By comparing individual contour segments to the curves in the database, accurate pattern matching was achieved.