FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3571493
Citations
2
Primary Topic
Anomaly Detection Techniques and Applications
Type
article
Field-Weighted Citation Impact
4.96

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article

FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior

Liang-Bi Chen, Xiang-Rui Huang
2 citations
IEEE Access
Anomaly Detection Techniques and Applications
4.96
article

FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior

Liang-Bi Chen, Xiang-Rui Huang
article en
2 citations

Abstract

No abstract available for this paper.
IEEE AccessVol. 13
National Penghu University of Science and Technology (TW)
National Science and Technology Council
Life below water
Openalex Percentile: Top 5%
Anomaly Detection Techniques and Applications
4.96
DOIOpen Access PDFOpenAlex
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FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior — Liang-Bi Chen, Xiang-Rui Huang · IEEE Access (2025) | TGRS Research Map | TGRS