Detection Method of Marine Biological Objects Based on Image Enhancement and Improved YOLOv5S
Marine biological object detection is of great significance for the exploration and protection of underwater resources. There have been some achievements in visual inspection for specific objects based on machine learning. However, owing to the complex imaging environment, some problems, such as low...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/10/10/1503 |
| _version_ | 1797472356699996160 |
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| author | Peng Li Yibing Fan Zhengyang Cai Zhiyu Lyu Weijie Ren |
| author_facet | Peng Li Yibing Fan Zhengyang Cai Zhiyu Lyu Weijie Ren |
| author_sort | Peng Li |
| collection | DOAJ |
| description | Marine biological object detection is of great significance for the exploration and protection of underwater resources. There have been some achievements in visual inspection for specific objects based on machine learning. However, owing to the complex imaging environment, some problems, such as low accuracy and poor real-time performance, have appeared in these object detection methods. To solve these problems, this paper proposes a detection method of marine biological objects based on image enhancement and YOLOv5S. Contrast-limited adaptive histogram equalization is taken to solve the problems of underwater image distortion and blur, and we put forward an improved YOLOv5S to improve accuracy and real-time performance of object detection. Compared with YOLOv5S, coordinate attention and adaptive spatial feature fusion are added in the improved YOLOv5S, which can accurately locate the target of interest and fully fuse the features of different scales. In addition, soft non-maximum suppression is adopted to replace non-maximum suppression for the improvement of the detection ability for overlapping objects. The experimental results show that the contrast-limited adaptive histogram equalization algorithm can effectively improve the underwater image quality and the detection accuracy. Compared with the original model (YOLOv5S), the proposed algorithm has a higher detection accuracy. The detection accuracy AP50 reaches 94.9% and the detection speed is 82 frames per second; therefore, the real-time performance can be said to reach a high level. |
| first_indexed | 2024-03-09T20:00:42Z |
| format | Article |
| id | doaj.art-2980cfe756e74473a6be732288cc7d88 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T20:00:42Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-2980cfe756e74473a6be732288cc7d882023-11-24T00:45:13ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-10-011010150310.3390/jmse10101503Detection Method of Marine Biological Objects Based on Image Enhancement and Improved YOLOv5SPeng Li0Yibing Fan1Zhengyang Cai2Zhiyu Lyu3Weijie Ren4College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaSchool of Automation Engineering, Northeast Electric Power University, Jilin 132012, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaMarine biological object detection is of great significance for the exploration and protection of underwater resources. There have been some achievements in visual inspection for specific objects based on machine learning. However, owing to the complex imaging environment, some problems, such as low accuracy and poor real-time performance, have appeared in these object detection methods. To solve these problems, this paper proposes a detection method of marine biological objects based on image enhancement and YOLOv5S. Contrast-limited adaptive histogram equalization is taken to solve the problems of underwater image distortion and blur, and we put forward an improved YOLOv5S to improve accuracy and real-time performance of object detection. Compared with YOLOv5S, coordinate attention and adaptive spatial feature fusion are added in the improved YOLOv5S, which can accurately locate the target of interest and fully fuse the features of different scales. In addition, soft non-maximum suppression is adopted to replace non-maximum suppression for the improvement of the detection ability for overlapping objects. The experimental results show that the contrast-limited adaptive histogram equalization algorithm can effectively improve the underwater image quality and the detection accuracy. Compared with the original model (YOLOv5S), the proposed algorithm has a higher detection accuracy. The detection accuracy AP50 reaches 94.9% and the detection speed is 82 frames per second; therefore, the real-time performance can be said to reach a high level.https://www.mdpi.com/2077-1312/10/10/1503marine biological objectobject detectionimage enhancementdeep learningimproved YOLOv5S |
| spellingShingle | Peng Li Yibing Fan Zhengyang Cai Zhiyu Lyu Weijie Ren Detection Method of Marine Biological Objects Based on Image Enhancement and Improved YOLOv5S Journal of Marine Science and Engineering marine biological object object detection image enhancement deep learning improved YOLOv5S |
| title | Detection Method of Marine Biological Objects Based on Image Enhancement and Improved YOLOv5S |
| title_full | Detection Method of Marine Biological Objects Based on Image Enhancement and Improved YOLOv5S |
| title_fullStr | Detection Method of Marine Biological Objects Based on Image Enhancement and Improved YOLOv5S |
| title_full_unstemmed | Detection Method of Marine Biological Objects Based on Image Enhancement and Improved YOLOv5S |
| title_short | Detection Method of Marine Biological Objects Based on Image Enhancement and Improved YOLOv5S |
| title_sort | detection method of marine biological objects based on image enhancement and improved yolov5s |
| topic | marine biological object object detection image enhancement deep learning improved YOLOv5S |
| url | https://www.mdpi.com/2077-1312/10/10/1503 |
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