Journal
JOURNAL OF MARINE SCIENCE AND ENGINEERING
Volume 9, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/jmse9121357
Keywords
image recognition; ORB algorithm; tuna shoal searching; unmanned aerial vehicle
Categories
Funding
- National key R&D Program of China [2019YFD0901502, 2020YFD0901202]
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The study presented a tuna fish school recognition system that utilizes feature point extraction and matching. By combining the best feature point algorithms with the k-nearest neighbors algorithm, the system successfully achieves accurate recognition of free-swimming tuna fish schools.
Tuna fish school detection provides information on the fishing decisions of purse seine fleets. Here, we present a recognition system that included fish shoal image acquisition, point extraction, point matching, and data storage. Points are a crucial characteristic for images of free-swimming tuna schools, and point algorithm analysis and point matching were studied for their applications in fish shoal recognition. The feature points were obtained by using one of the best point algorithms (scale invariant feature transform, speeded up robust features, oriented fast and rotated brief). The k-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new points, which means that new data points will be assigned a value based on how closely they match the points that exist in the database. Finally, we tested the model, and the experimental results show that the proposed method can accurately and effectively recognize tuna free-swimming schools.
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