An underwater target recognition method based on improved YOLOv4 in complex marine environment
In the marine environment, there are problems such as complex background and low illumination, resulting in poor picture quality, and the aggregation of small targets and multiple targets brings difficulties to target recognition. In order to improve the accuracy of marine target detection, the imag...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2022.2082579 |
| _version_ | 1818470080851214336 |
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| author | Jili Zhou Qing Yang Huijuan Meng Dexin Gao |
| author_facet | Jili Zhou Qing Yang Huijuan Meng Dexin Gao |
| author_sort | Jili Zhou |
| collection | DOAJ |
| description | In the marine environment, there are problems such as complex background and low illumination, resulting in poor picture quality, and the aggregation of small targets and multiple targets brings difficulties to target recognition. In order to improve the accuracy of marine target detection, the image enhancement and improved YOLOv4 algorithm are used to identify marine organisms. Firstly, aiming at the problems of image blur, some of the images are enhanced with the multi-scale retinex with colour restoration (MSRCR) enhancement algorithm so that the image is clearer and it is easier to extract features. Secondly, Mosaic augmentation is added to YOLOv4 to enrich the data set and increase network robustness. Then the Spatial Pyramid Pooling (SPP) module of YOLOv4 is improved by changing the size of the pooling core, which increases the range of feature extraction and improves the detection capabilities, and its mAP value reaches 97.06%. The experimental results show that the detection accuracy of the image enhancement and improved YOLOv4 algorithm is 7.16% higher than that of the original algorithm. And the improved YOLOv4 algorithm is improved in average precision and recall rate compared with other algorithms, which verifies the effectiveness of the algorithm. |
| first_indexed | 2024-04-13T21:32:46Z |
| format | Article |
| id | doaj.art-cb0d714cd0d94daa8e73185552e6c812 |
| institution | Directory Open Access Journal |
| issn | 2164-2583 |
| language | English |
| last_indexed | 2024-04-13T21:32:46Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj.art-cb0d714cd0d94daa8e73185552e6c8122022-12-22T02:29:05ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832022-12-0110159060210.1080/21642583.2022.2082579An underwater target recognition method based on improved YOLOv4 in complex marine environmentJili Zhou0Qing Yang1Huijuan Meng2Dexin Gao3College of Information Science and Technology, Qingdao University of Science & Technology, Qingdao, People’s Republic of ChinaCollege of Information Science and Technology, Qingdao University of Science & Technology, Qingdao, People’s Republic of ChinaCollege of Information Science and Technology, Qingdao University of Science & Technology, Qingdao, People’s Republic of ChinaCollege of Automation and Electronic Engineering, Qingdao University of Science & Technology, Qingdao, People’s Republic of ChinaIn the marine environment, there are problems such as complex background and low illumination, resulting in poor picture quality, and the aggregation of small targets and multiple targets brings difficulties to target recognition. In order to improve the accuracy of marine target detection, the image enhancement and improved YOLOv4 algorithm are used to identify marine organisms. Firstly, aiming at the problems of image blur, some of the images are enhanced with the multi-scale retinex with colour restoration (MSRCR) enhancement algorithm so that the image is clearer and it is easier to extract features. Secondly, Mosaic augmentation is added to YOLOv4 to enrich the data set and increase network robustness. Then the Spatial Pyramid Pooling (SPP) module of YOLOv4 is improved by changing the size of the pooling core, which increases the range of feature extraction and improves the detection capabilities, and its mAP value reaches 97.06%. The experimental results show that the detection accuracy of the image enhancement and improved YOLOv4 algorithm is 7.16% higher than that of the original algorithm. And the improved YOLOv4 algorithm is improved in average precision and recall rate compared with other algorithms, which verifies the effectiveness of the algorithm.https://www.tandfonline.com/doi/10.1080/21642583.2022.2082579Complex marine environmentunderwater target recognitionimproved YOLOv4image enhancement |
| spellingShingle | Jili Zhou Qing Yang Huijuan Meng Dexin Gao An underwater target recognition method based on improved YOLOv4 in complex marine environment Systems Science & Control Engineering Complex marine environment underwater target recognition improved YOLOv4 image enhancement |
| title | An underwater target recognition method based on improved YOLOv4 in complex marine environment |
| title_full | An underwater target recognition method based on improved YOLOv4 in complex marine environment |
| title_fullStr | An underwater target recognition method based on improved YOLOv4 in complex marine environment |
| title_full_unstemmed | An underwater target recognition method based on improved YOLOv4 in complex marine environment |
| title_short | An underwater target recognition method based on improved YOLOv4 in complex marine environment |
| title_sort | underwater target recognition method based on improved yolov4 in complex marine environment |
| topic | Complex marine environment underwater target recognition improved YOLOv4 image enhancement |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2022.2082579 |
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