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Fish Target Detection Method Based on EPSA-CenterNet2

Published: 09 November 2022 Publication History

Abstract

At present, small target detection and target detection under complex backgrounds are still a major difficulty in the field of image target detection. However, fish image detection scenes often contain complex backgrounds such as water grass and reef, and fish form is small. In order to overcome the problem which is low accuracy of detection of small fish targets in complex backgrounds, In this paper, a Center Point Network 2 with Efficient Pyramid Split Attention (EPSA-CenterNet2) was proposed. The Network incorporated an Efficient Pyramid Split Attention Network (EPSANet) into CenterNet2 to improve small target detection accuracy in complex environments. In this paper, 149 images of the oplegnathus punctatus were used as a dataset to train EPSA-CenterNet2 and four other mainstream target detection networks. The experimental results showed that EPSA-CenterNet2 was superior to CenterNet2, YOLOv3, YOLOv5 and SSD in the average accuracy including AP and AP50, and the number of missed targets in small target images was less. Therefore, EPSA-Centernet2 can detect fish image targets in complex backgrounds more accurately.

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ICCCV '22: Proceedings of the 5th International Conference on Control and Computer Vision
August 2022
241 pages
ISBN:9781450397315
DOI:10.1145/3561613
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 09 November 2022

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Author Tags

  1. CenterNet2
  2. attention network
  3. small target
  4. target detection

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