Object recognition algorithm for the automatic identification and removal of invasive fish.
Invasive fish species are a growing threat worldwide, causing great harm to biodiversity and ecosystems, and leading to large economic losses. As the most introduced group of aquatic animals in the world, fish are also one of the most threatened. For species that are considered invasive, removing them is the best way to reduce the long-term cost of eradication or control. This paper proposes an object recognition algorithm to automatically identify fish species. Our previous work on general object recognition, called Evolution-COnstructed (ECO) Features, was modified and adapted to construct features and use AdaBoost to classify different fish species. The proposed algorithm does not depend on human experts to design features for fish species classification, but constructs efficient features automatically. Results from experiments show the proposed method obtained an average of 98.9% classification accuracy with a standard deviation of 0.96% with a dataset composed of 8 fish species and a total of 1049 images. Using this algorithm, a fish monitoring system can be built to remove invasive species and monitor native fish abundance, distribution, and size with minimal collateral impact and fish suffering.