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UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection
Underwater video object detection is a challenging task due to the poor quality of underwater videos, including blurriness and low contrast. In recent years, Yolo series models have been widely applied to underwater video object detection. However, these models perform poorly for blurry and low-cont...
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/PMC10221615/ https://www.ncbi.nlm.nih.gov/pubmed/37430773 http://dx.doi.org/10.3390/s23104859 |
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author | Pan, Haixia Lan, Jiahua Wang, Hongqiang Li, Yanan Zhang, Meng Ma, Mojie Zhang, Dongdong Zhao, Xiaoran |
author_facet | Pan, Haixia Lan, Jiahua Wang, Hongqiang Li, Yanan Zhang, Meng Ma, Mojie Zhang, Dongdong Zhao, Xiaoran |
author_sort | Pan, Haixia |
collection | PubMed |
description | Underwater video object detection is a challenging task due to the poor quality of underwater videos, including blurriness and low contrast. In recent years, Yolo series models have been widely applied to underwater video object detection. However, these models perform poorly for blurry and low-contrast underwater videos. Additionally, they fail to account for the contextual relationships between the frame-level results. To address these challenges, we propose a video object detection model named UWV-Yolox. First, the Contrast Limited Adaptive Histogram Equalization method is used to augment the underwater videos. Then, a new CSP_CA module is proposed by adding Coordinate Attention to the backbone of the model to augment the representations of objects of interest. Next, a new loss function is proposed, including regression and jitter loss. Finally, a frame-level optimization module is proposed to optimize the detection results by utilizing the relationship between neighboring frames in videos, improving the video detection performance. To evaluate the performance of our model, We construct experiments on the UVODD dataset built in the paper, and select mAP@0.5 as the evaluation metric. The mAP@0.5 of the UWV-Yolox model reaches 89.0%, which is 3.2% better than the original Yolox model. Furthermore, compared with other object detection models, the UWV-Yolox model has more stable predictions for objects, and our improvements can be flexibly applied to other models. |
format | Online Article Text |
id | pubmed-10221615 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102216152023-05-28 UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection Pan, Haixia Lan, Jiahua Wang, Hongqiang Li, Yanan Zhang, Meng Ma, Mojie Zhang, Dongdong Zhao, Xiaoran Sensors (Basel) Article Underwater video object detection is a challenging task due to the poor quality of underwater videos, including blurriness and low contrast. In recent years, Yolo series models have been widely applied to underwater video object detection. However, these models perform poorly for blurry and low-contrast underwater videos. Additionally, they fail to account for the contextual relationships between the frame-level results. To address these challenges, we propose a video object detection model named UWV-Yolox. First, the Contrast Limited Adaptive Histogram Equalization method is used to augment the underwater videos. Then, a new CSP_CA module is proposed by adding Coordinate Attention to the backbone of the model to augment the representations of objects of interest. Next, a new loss function is proposed, including regression and jitter loss. Finally, a frame-level optimization module is proposed to optimize the detection results by utilizing the relationship between neighboring frames in videos, improving the video detection performance. To evaluate the performance of our model, We construct experiments on the UVODD dataset built in the paper, and select mAP@0.5 as the evaluation metric. The mAP@0.5 of the UWV-Yolox model reaches 89.0%, which is 3.2% better than the original Yolox model. Furthermore, compared with other object detection models, the UWV-Yolox model has more stable predictions for objects, and our improvements can be flexibly applied to other models. MDPI 2023-05-18 /pmc/articles/PMC10221615/ /pubmed/37430773 http://dx.doi.org/10.3390/s23104859 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 Pan, Haixia Lan, Jiahua Wang, Hongqiang Li, Yanan Zhang, Meng Ma, Mojie Zhang, Dongdong Zhao, Xiaoran UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection |
title | UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection |
title_full | UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection |
title_fullStr | UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection |
title_full_unstemmed | UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection |
title_short | UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection |
title_sort | uwv-yolox: a deep learning model for underwater video object detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10221615/ https://www.ncbi.nlm.nih.gov/pubmed/37430773 http://dx.doi.org/10.3390/s23104859 |
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