Intelligent Recognition Research for Fish Behavior

A special issue of Fishes (ISSN 2410-3888).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 413

Special Issue Editors


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Guest Editor
Department of Biosystems Engineering, Zhejiang University, Hangzhou 310000, China
Interests: computational fish behavior; interaction between fish behavior and environment
National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China
Interests: computer vision; robotics; AI in smart fishery

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Guest Editor
College of Mechanical and Electronic Engineering, Dalian Minzu University, Dalian 116600, China
Interests: intelligent fisheries; underwater image processing; fish pose detection; fish feeding behavior analysis; lightweighting of deep learning models; fish counting; fishmeal detection

Special Issue Information

Dear Colleagues,

The Special Issue "Intelligent Recognition Research for Fish Behavior" focuses on the latest developments in using intelligent and automated techniques for recognizing and analyzing the behavior of fish. Fish behavior is an important area of study for understanding aquatic ecosystems, managing fisheries, and monitoring animal welfare in aquaculture.

The specific research topic is dedicated to (1) enhancing the understanding of aquatic ecosystems—fish are a critical component of aquatic ecosystems, and the study of their behavior can help us better understand and manage the entire aquatic ecosystem; (2) supporting the sustainable utilization of fishery resources; (3) promoting the development of aquaculture—the results of fish behavior research can provide important theoretical guidance for the aquaculture industry, optimize feeding and management, and improve farming efficiency and product quality. This Special Issue aims to showcase applications of machine learning, computer vision, and other state-of-the-art intelligent methods to the study of fish behavior recognition. It calls for original and novel papers related to the following research topics:

  1. Automated detection, classification, and identification of fish;
  2. Tracking of individual fish movements and behaviors;
  3. Analyzing group dynamics and schooling behaviors;
  4. Monitoring fish health and stress levels;
  5. Assessing the impacts of environmental changes on fish.

Dr. Jian Zhao
Dr. Ran Zhao
Dr. Kewei Cai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fishes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fish behavior
  • aquatic ecosystems
  • fishery resources
  • aquaculture
  • intelligent recognition
  • fish welfare

Published Papers (1 paper)

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Research

22 pages, 11091 KiB  
Article
RTL-YOLOv8n: A Lightweight Model for Efficient and Accurate Underwater Target Detection
by Guanbo Feng, Zhixin Xiong, Hongshuai Pang, Yunlei Gao, Zhiqiang Zhang, Jiapeng Yang and Zhihong Ma
Fishes 2024, 9(8), 294; https://doi.org/10.3390/fishes9080294 - 24 Jul 2024
Viewed by 228
Abstract
Underwater object detection is essential for the advancement of automated aquaculture operations. Addressing the challenges of low detection accuracy and insufficient generalization capabilities for underwater targets, this paper focuses on the development of a novel detection method tailored to such environments. We introduce [...] Read more.
Underwater object detection is essential for the advancement of automated aquaculture operations. Addressing the challenges of low detection accuracy and insufficient generalization capabilities for underwater targets, this paper focuses on the development of a novel detection method tailored to such environments. We introduce the RTL-YOLOv8n model, specifically designed to enhance the precision and efficiency of detecting objects underwater. This model incorporates advanced feature-extraction mechanisms—RetBlock and triplet attention—that significantly improve its ability to discern fine details amidst complex underwater scenes. Additionally, the model employs a lightweight coupled detection head (LCD-Head), which reduces its computational requirements by 31.6% compared to the conventional YOLOv8n, without sacrificing performance. Enhanced by the Focaler–MPDIoU loss function, RTL-YOLOv8n demonstrates superior capability in detecting challenging targets, showing a 1.5% increase in [email protected] and a 5.2% improvement in precision over previous models. These results not only confirm the effectiveness of RTL-YOLOv8n in complex underwater environments but also highlight its potential applicability in other settings requiring efficient and precise object detection. This research provides valuable insights into the development of aquatic life detection and contributes to the field of smart aquatic monitoring systems. Full article
(This article belongs to the Special Issue Intelligent Recognition Research for Fish Behavior)
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