2022
DOI: 10.3389/fmars.2022.933735
|View full text |Cite
|
Sign up to set email alerts
|

Research on target detection of Engraulis japonicus purse seine based on improved model of YOLOv5

Abstract: The refined monitoring and identification of fishing operations by fishing vessels is of great significance and value to fishing vessels. In order to solve the problem of inaccurate statistics of current Engraulis japonicus fishing quota and classification, this paper proposes an improved identification algorithm based on YOLOv5. This method introduces the SENet attention mechanism into the YOLOv5 backbone network structure, integrates the target information in different periods of fishing operations, reduces … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
12
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
6
1

Relationship

1
6

Authors

Journals

citations
Cited by 10 publications
(12 citation statements)
references
References 26 publications
0
12
0
Order By: Relevance
“…The TAC value would be referred to the calculation value of the VMS. In the next step, electronic monitoring (Wang et al ., 2022; Zhang et al ., 2022) technology will be applied for the research.…”
Section: Discussionmentioning
confidence: 99%
“…The TAC value would be referred to the calculation value of the VMS. In the next step, electronic monitoring (Wang et al ., 2022; Zhang et al ., 2022) technology will be applied for the research.…”
Section: Discussionmentioning
confidence: 99%
“…The 28, 770 position data marked with the operation statuses were divided into a training set and a test set at 4: 1. The training set data was used as input into the neural network and iterated for 100 rounds to train the fishing vessels' operational status classification model (Wang et al, 2022;Zhang et al, 2022). where q(x i ) is the value, p(x i ) is the corresponding probability value.…”
Section: The Operational Status Classification Modelmentioning
confidence: 99%
“…Advancements in computer vision and machine learning (ML) technology offer vast potential to improve upon the reliability of detections in real-time, and to automate much of the surveillance process. Modern intelligent algorithms can learn to accurately and consistently locate and identify objects in complex scenes, but do not tire like human observers and can be orders of magnitude more efficient once deployed (e.g., Longmore et al, 2017;Hodgson et al, 2018;Burr et al, 2019;Eikelboom et al, 2019;Zhang et al, 2022). Concurrently, it is expected that drone technology and associated systems (including regulations) will continue to progress rapidly, which will allow monitoring and surveillance operations to be automated end-to-end.…”
Section: The Rise Of the Machinesmentioning
confidence: 99%
“…We believe that ML-driven shark species detectors will be excellent decision-support tools for beach managers in the very near future. The technology is already changing the way ecologists survey marine fauna (e.g., Butcher et al, 2021;Dujon et al, 2021;Jenrette et al, 2022;Marrable et al, 2022;Zhang et al, 2022;Shi et al, 2022). Humans will always need to be included in the decision loop, but the human role will change as the ML models become more reliable and flight systems become more automated.…”
Section: Vision For the Futurementioning
confidence: 99%