Detection of the Sinking State of Liquid Oil in Breaking Waves Based on Synthesized Data: A Behavior Process Study of Sunken and Submerged Oil
In computer vision, pollutant detection is a highly concerning issue, and it has been widely used in the fields of pollutant identification, tracking, and precise positioning. In the ocean, oil tends to disperse into the water column as droplets under breaking waves, and it is called sunken and subm...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/10/5/604 |
| _version_ | 1797498826309763072 |
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| author | Shibiao Fang Lin Mu Kuan Liu Darong Liu |
| author_facet | Shibiao Fang Lin Mu Kuan Liu Darong Liu |
| author_sort | Shibiao Fang |
| collection | DOAJ |
| description | In computer vision, pollutant detection is a highly concerning issue, and it has been widely used in the fields of pollutant identification, tracking, and precise positioning. In the ocean, oil tends to disperse into the water column as droplets under breaking waves, and it is called sunken and submerged oil. Aiming at the most difficult issue of identifying liquid submerged oil pollution, this paper proposes a method of synthesized data containing specific markers for oil detection. The Canny operator was used to remove the background of the liquid submerged oil. Then, affine transformation was applied to simulate the real situation of oil deformation. Linear mapping was presented by matrix multiplication, and translation was represented by vector addition. At last, bilinear interpolation was used to integrate the oil into the image of the laboratory pictures. In addition, this research randomly added interference information, so that the probability distribution of synthesized data was closer to the probability distribution of the real data. Then, this paper combined various methods to improve the accuracy of liquid oil detection, such as Feature Pyramid Networks, RoIAlign, difficult sample mining. Based on the above methods, 1838 images were synthesized in this paper and combined into a training set. The results show that the average accuracy of the oil detection is increased by 79.72%. The accuracy of the synthesized data method for labeled oil detection was 18.56% higher than that of oil detection without labeling. This research solves the difficulty of obtaining sunken and submerged oil images and the high cost of image annotation. |
| first_indexed | 2024-03-10T03:38:56Z |
| format | Article |
| id | doaj.art-aa3b74901c774c67a73d40d9fb267436 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T03:38:56Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-aa3b74901c774c67a73d40d9fb2674362023-11-23T11:39:05ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-04-0110560410.3390/jmse10050604Detection of the Sinking State of Liquid Oil in Breaking Waves Based on Synthesized Data: A Behavior Process Study of Sunken and Submerged OilShibiao Fang0Lin Mu1Kuan Liu2Darong Liu3College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaCollege of Marine Science and Technology, China University of Geosciences, 388 Lumo Road, Wuhan 430074, ChinaIn computer vision, pollutant detection is a highly concerning issue, and it has been widely used in the fields of pollutant identification, tracking, and precise positioning. In the ocean, oil tends to disperse into the water column as droplets under breaking waves, and it is called sunken and submerged oil. Aiming at the most difficult issue of identifying liquid submerged oil pollution, this paper proposes a method of synthesized data containing specific markers for oil detection. The Canny operator was used to remove the background of the liquid submerged oil. Then, affine transformation was applied to simulate the real situation of oil deformation. Linear mapping was presented by matrix multiplication, and translation was represented by vector addition. At last, bilinear interpolation was used to integrate the oil into the image of the laboratory pictures. In addition, this research randomly added interference information, so that the probability distribution of synthesized data was closer to the probability distribution of the real data. Then, this paper combined various methods to improve the accuracy of liquid oil detection, such as Feature Pyramid Networks, RoIAlign, difficult sample mining. Based on the above methods, 1838 images were synthesized in this paper and combined into a training set. The results show that the average accuracy of the oil detection is increased by 79.72%. The accuracy of the synthesized data method for labeled oil detection was 18.56% higher than that of oil detection without labeling. This research solves the difficulty of obtaining sunken and submerged oil images and the high cost of image annotation.https://www.mdpi.com/2077-1312/10/5/604oil spillsubmerged liquid oilsynthesized datalaboratory environmentspollutant behavior detection |
| spellingShingle | Shibiao Fang Lin Mu Kuan Liu Darong Liu Detection of the Sinking State of Liquid Oil in Breaking Waves Based on Synthesized Data: A Behavior Process Study of Sunken and Submerged Oil Journal of Marine Science and Engineering oil spill submerged liquid oil synthesized data laboratory environments pollutant behavior detection |
| title | Detection of the Sinking State of Liquid Oil in Breaking Waves Based on Synthesized Data: A Behavior Process Study of Sunken and Submerged Oil |
| title_full | Detection of the Sinking State of Liquid Oil in Breaking Waves Based on Synthesized Data: A Behavior Process Study of Sunken and Submerged Oil |
| title_fullStr | Detection of the Sinking State of Liquid Oil in Breaking Waves Based on Synthesized Data: A Behavior Process Study of Sunken and Submerged Oil |
| title_full_unstemmed | Detection of the Sinking State of Liquid Oil in Breaking Waves Based on Synthesized Data: A Behavior Process Study of Sunken and Submerged Oil |
| title_short | Detection of the Sinking State of Liquid Oil in Breaking Waves Based on Synthesized Data: A Behavior Process Study of Sunken and Submerged Oil |
| title_sort | detection of the sinking state of liquid oil in breaking waves based on synthesized data a behavior process study of sunken and submerged oil |
| topic | oil spill submerged liquid oil synthesized data laboratory environments pollutant behavior detection |
| url | https://www.mdpi.com/2077-1312/10/5/604 |
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