Rethinking Underwater Crab Detection via Defogging and Channel Compensation
Crab aquaculture is an important component of the freshwater aquaculture industry in China, encompassing an expansive farming area of over 6000 km<sup>2</sup> nationwide. Currently, crab farmers rely on manually monitored feeding platforms to count the number and assess the distribution...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Fishes |
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| Online Access: | https://www.mdpi.com/2410-3888/9/2/60 |
| _version_ | 1827343604036665344 |
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| author | Yueping Sun Bikang Yuan Ziqiang Li Yong Liu Dean Zhao |
| author_facet | Yueping Sun Bikang Yuan Ziqiang Li Yong Liu Dean Zhao |
| author_sort | Yueping Sun |
| collection | DOAJ |
| description | Crab aquaculture is an important component of the freshwater aquaculture industry in China, encompassing an expansive farming area of over 6000 km<sup>2</sup> nationwide. Currently, crab farmers rely on manually monitored feeding platforms to count the number and assess the distribution of crabs in the pond. However, this method is inefficient and lacks automation. To address the problem of efficient and rapid detection of crabs via automated systems based on machine vision in low-brightness underwater environments, a two-step color correction and improved dark channel prior underwater image processing approach for crab detection is proposed in this paper. Firstly, the parameters of the dark channel prior are optimized with guided filtering and quadtrees to solve the problems of blurred underwater images and artificial lighting. Then, the gray world assumption, the perfect reflection assumption, and a strong channel to compensate for the weak channel are applied to improve the pixels of red and blue channels, correct the color of the defogged image, optimize the visual effect of the image, and enrich the image information. Finally, ShuffleNetV2 is applied to optimize the target detection model to improve the model detection speed and real-time performance. The experimental results show that the proposed method has a detection rate of 90.78% and an average confidence level of 0.75. Compared with the improved YOLOv5s detection results of the original image, the detection rate of the proposed method is increased by 21.41%, and the average confidence level is increased by 47.06%, which meets a good standard. This approach could effectively build an underwater crab distribution map and provide scientific guidance for crab farming. |
| first_indexed | 2024-03-07T22:32:38Z |
| format | Article |
| id | doaj.art-6b8ac279de5840309e0ab52be2ac5909 |
| institution | Directory Open Access Journal |
| issn | 2410-3888 |
| language | English |
| last_indexed | 2024-03-07T22:32:38Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fishes |
| spelling | doaj.art-6b8ac279de5840309e0ab52be2ac59092024-02-23T15:16:08ZengMDPI AGFishes2410-38882024-01-01926010.3390/fishes9020060Rethinking Underwater Crab Detection via Defogging and Channel CompensationYueping Sun0Bikang Yuan1Ziqiang Li2Yong Liu3Dean Zhao4School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaCrab aquaculture is an important component of the freshwater aquaculture industry in China, encompassing an expansive farming area of over 6000 km<sup>2</sup> nationwide. Currently, crab farmers rely on manually monitored feeding platforms to count the number and assess the distribution of crabs in the pond. However, this method is inefficient and lacks automation. To address the problem of efficient and rapid detection of crabs via automated systems based on machine vision in low-brightness underwater environments, a two-step color correction and improved dark channel prior underwater image processing approach for crab detection is proposed in this paper. Firstly, the parameters of the dark channel prior are optimized with guided filtering and quadtrees to solve the problems of blurred underwater images and artificial lighting. Then, the gray world assumption, the perfect reflection assumption, and a strong channel to compensate for the weak channel are applied to improve the pixels of red and blue channels, correct the color of the defogged image, optimize the visual effect of the image, and enrich the image information. Finally, ShuffleNetV2 is applied to optimize the target detection model to improve the model detection speed and real-time performance. The experimental results show that the proposed method has a detection rate of 90.78% and an average confidence level of 0.75. Compared with the improved YOLOv5s detection results of the original image, the detection rate of the proposed method is increased by 21.41%, and the average confidence level is increased by 47.06%, which meets a good standard. This approach could effectively build an underwater crab distribution map and provide scientific guidance for crab farming.https://www.mdpi.com/2410-3888/9/2/60underwater crab image processingdark channel priorcolor correctionchannel compensationtarget detection |
| spellingShingle | Yueping Sun Bikang Yuan Ziqiang Li Yong Liu Dean Zhao Rethinking Underwater Crab Detection via Defogging and Channel Compensation Fishes underwater crab image processing dark channel prior color correction channel compensation target detection |
| title | Rethinking Underwater Crab Detection via Defogging and Channel Compensation |
| title_full | Rethinking Underwater Crab Detection via Defogging and Channel Compensation |
| title_fullStr | Rethinking Underwater Crab Detection via Defogging and Channel Compensation |
| title_full_unstemmed | Rethinking Underwater Crab Detection via Defogging and Channel Compensation |
| title_short | Rethinking Underwater Crab Detection via Defogging and Channel Compensation |
| title_sort | rethinking underwater crab detection via defogging and channel compensation |
| topic | underwater crab image processing dark channel prior color correction channel compensation target detection |
| url | https://www.mdpi.com/2410-3888/9/2/60 |
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