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Deep-Sea Biological Detection Method Based on Lightweight YOLOv5n
Deep-sea biological detection is essential for deep-sea resource research and conservation. However, due to the poor image quality and insufficient image samples in the complex deep-sea imaging environment, resulting in poor detection results. Furthermore, most existing detection models accomplish h...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611201/ https://www.ncbi.nlm.nih.gov/pubmed/37896693 http://dx.doi.org/10.3390/s23208600 |
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author | Ding, Zhongjun Liu, Chen Li, Dewei Yi, Guangrui |
author_facet | Ding, Zhongjun Liu, Chen Li, Dewei Yi, Guangrui |
author_sort | Ding, Zhongjun |
collection | PubMed |
description | Deep-sea biological detection is essential for deep-sea resource research and conservation. However, due to the poor image quality and insufficient image samples in the complex deep-sea imaging environment, resulting in poor detection results. Furthermore, most existing detection models accomplish high precision at the expense of increased complexity, and leading cannot be well deployed in the deep-sea environment. To alleviate these problems, a detection method for deep-sea organisms based on lightweight YOLOv5n is proposed. First, a lightweight YOLOv5n is created. The proposed image enhancement method based on global and local contrast fusion (GLCF) is introduced into the input layer of YOLOv5n to address the problem of color deviation and low contrast in the image. At the same time, a Bottleneck based on the Ghost module and simAM (GS-Bottleneck) is developed to achieve a lightweight model while ensuring sure detection performance. Second, a transfer learning strategy combined with knowledge distillation (TLKD) is designed, which can reduce the dependence of the model on the amount of data and improve the generalization ability to enhance detection accuracy. Experimental results on the deep-sea biological dataset show that the proposed method achieves good detection accuracy and speed, outperforming existing methods. |
format | Online Article Text |
id | pubmed-10611201 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106112012023-10-28 Deep-Sea Biological Detection Method Based on Lightweight YOLOv5n Ding, Zhongjun Liu, Chen Li, Dewei Yi, Guangrui Sensors (Basel) Article Deep-sea biological detection is essential for deep-sea resource research and conservation. However, due to the poor image quality and insufficient image samples in the complex deep-sea imaging environment, resulting in poor detection results. Furthermore, most existing detection models accomplish high precision at the expense of increased complexity, and leading cannot be well deployed in the deep-sea environment. To alleviate these problems, a detection method for deep-sea organisms based on lightweight YOLOv5n is proposed. First, a lightweight YOLOv5n is created. The proposed image enhancement method based on global and local contrast fusion (GLCF) is introduced into the input layer of YOLOv5n to address the problem of color deviation and low contrast in the image. At the same time, a Bottleneck based on the Ghost module and simAM (GS-Bottleneck) is developed to achieve a lightweight model while ensuring sure detection performance. Second, a transfer learning strategy combined with knowledge distillation (TLKD) is designed, which can reduce the dependence of the model on the amount of data and improve the generalization ability to enhance detection accuracy. Experimental results on the deep-sea biological dataset show that the proposed method achieves good detection accuracy and speed, outperforming existing methods. MDPI 2023-10-20 /pmc/articles/PMC10611201/ /pubmed/37896693 http://dx.doi.org/10.3390/s23208600 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ding, Zhongjun Liu, Chen Li, Dewei Yi, Guangrui Deep-Sea Biological Detection Method Based on Lightweight YOLOv5n |
title | Deep-Sea Biological Detection Method Based on Lightweight YOLOv5n |
title_full | Deep-Sea Biological Detection Method Based on Lightweight YOLOv5n |
title_fullStr | Deep-Sea Biological Detection Method Based on Lightweight YOLOv5n |
title_full_unstemmed | Deep-Sea Biological Detection Method Based on Lightweight YOLOv5n |
title_short | Deep-Sea Biological Detection Method Based on Lightweight YOLOv5n |
title_sort | deep-sea biological detection method based on lightweight yolov5n |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10611201/ https://www.ncbi.nlm.nih.gov/pubmed/37896693 http://dx.doi.org/10.3390/s23208600 |
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