Pubblicazione scientifica

Fish Keypoints Detection for Ecology Monitoring Based on Underwater Visual Intelligence

FILONE TECNOLOGICO	TEMA
economia circolare e sostenibilità

RIASSUNTO

This paper introduces a fishery ecology monitoring system for cultivation pools, and proposes a new stereo keypoint detection method followed by curve fitting analysis to estimate the fish posture and length. The system, which can be employed for aquaculture monitoring, is featured by its exploitation of underwater visual intelligence and deep neural-network architecture. As input, stereo image pairs are obtained by underwater binocular camera. A deep neural-network under Faster R-CNN architecture is built to detect fish from the stereo image inputs. Another network under Stacked Hourglass architecture is constructed to detect specific keypoints of each fish. For ecology monitoring, detected keypoints are used in the estimation of the fishes' posture and length. Unlike other size estimation methods which also apply a binocular camera, our method naturally bypasses the pixel-wise matching difficulty in global stereo matching algorithms. Experiment shows that our system is applicable for online fish ecology monitoring, with efficient and accurate estimation performance.

DATA

Data di pubblicazione: 13/12/2020

AUTORI

FEIYANG SUO
KANGWEI HUANG
GUI LING
YANJUN LI
JI XIANG

ENTE DI AFFERENZA

COLLEGE OF CTRL SCIENCE & ENG ZHEJIANG UNIVERSITYHANGZHOUCHINA310027
SCHOOL OF INFO & ELEC ENG ZHEJIANG UNIV CITY COLLEGEHANGZHOUCHINA310015
COLLEGE OF ELEC ENG ZHEJIANG UNIVERSITYHANGZHOUCHINA310027

RIVISTA

2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV) (Page(s): 542-547)

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