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Cited 3 time in webofscience Cited 13 time in scopus
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A Study on Enhancement of Fish Recognition Using Cumulative Mean of YOLO Network in Underwater Video Imagesopen access

Authors
Park, Jin-HyunKang, Changgu
Issue Date
Nov-2020
Publisher
MDPI
Keywords
exotic invasive species; object classification; video image; YOLO
Citation
JOURNAL OF MARINE SCIENCE AND ENGINEERING, v.8, no.11
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF MARINE SCIENCE AND ENGINEERING
Volume
8
Number
11
URI
https://scholarworks.bwise.kr/gnu/handle/sw.gnu/5959
DOI
10.3390/jmse8110952
ISSN
2077-1312
Abstract
In the underwater environment, in order to preserve rare and endangered objects or to eliminate the exotic invasive species that can destroy the ecosystems, it is essential to classify objects and estimate their number. It is very difficult to classify objects and estimate their number. While YOLO shows excellent performance in object recognition, it recognizes objects by processing the images of each frame independently of each other. By accumulating the object classification results from the past frames to the current frame, we propose a method to accurately classify objects, and count their number in sequential video images. This has a high classification probability of 93.94% and 97.06% in the test videos of Bluegill and Largemouth bass, respectively. The proposed method shows very good classification performance in video images taken of the underwater environment.
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융합기술공과대학 > Division of Mechatronics Engineering > Journal Articles

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융합기술공과대학 (메카트로닉스공학부)
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