Behavior Recognition of Squid Jigger Based on Deep Learning Article Swipe
Yifan Song
,
Shengmao Zhang
,
Fenghua Tang
,
Yongchuang Shi
,
Yumei Wu
,
Jianwen He
,
Yunyun Chen
,
Lin Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/fishes8100502
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/fishes8100502
In recent years, with the development of pelagic fishing, the working environment and monitoring of crew (squid jigger) members have become increasingly important. However, traditional methods of pelagic human observers suffer from high costs, low coverage, poor timeliness, and susceptibility to subjective factors. In contrast, the Electronic Monitoring System (EMS) has advantages such as continuous operation under various weather conditions; more objective, transparent, and efficient data; and less interference with fishing operations. This paper shows how the 3DCNN model, LSTM+ResNet model, and TimeSformer model are applied to video-classification tasks, and for the first time, they are applied to an EMS. In addition, this paper tests and compares the application effects of the three models on video classification, and discusses the advantages and challenges of using them for video recognition. Through experiments, we obtained the accuracy and relevant indicators of video recognition using different models. The research results show that when NUM_FRAMES is set to 8, the LSTM+ResNet-50 model has the best performance, with an accuracy of 88.47%, an F1 score of 0.8881, and an map score of 0.8133. Analyzing the EMS for pelagic fishing can improve China’s performance level and management efficiency in pelagic fishing, and promote the development of the fishery knowledge service system and smart fishery engineering.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/fishes8100502
- https://www.mdpi.com/2410-3888/8/10/502/pdf?version=1696767362
- OA Status
- gold
- Cited By
- 4
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387454308
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387454308Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/fishes8100502Digital Object Identifier
- Title
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Behavior Recognition of Squid Jigger Based on Deep LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-10-08Full publication date if available
- Authors
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Yifan Song, Shengmao Zhang, Fenghua Tang, Yongchuang Shi, Yumei Wu, Jianwen He, Yunyun Chen, Lin LiList of authors in order
- Landing page
-
https://doi.org/10.3390/fishes8100502Publisher landing page
- PDF URL
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https://www.mdpi.com/2410-3888/8/10/502/pdf?version=1696767362Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2410-3888/8/10/502/pdf?version=1696767362Direct OA link when available
- Concepts
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Pelagic zone, Crew, Fishing, Computer science, Identifier, Artificial intelligence, Machine learning, Real-time computing, Fishery, Engineering, Aeronautics, Programming language, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 1, 2023: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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