Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
In infrared images of the sea surface, apart from the complex background of the sea surface, there are often sky and island backgrounds. The disturbances caused by sea wind and the reflection of intense sunlight on the sea surface increase the complexity of the background, which seriously hinders th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/22/12247 |
| _version_ | 1797460355224436736 |
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| author | Dongming Lu Jiangyun Tan Mengke Wang Longyin Teng Liping Wang Guohua Gu |
| author_facet | Dongming Lu Jiangyun Tan Mengke Wang Longyin Teng Liping Wang Guohua Gu |
| author_sort | Dongming Lu |
| collection | DOAJ |
| description | In infrared images of the sea surface, apart from the complex background of the sea surface, there are often sky and island backgrounds. The disturbances caused by sea wind and the reflection of intense sunlight on the sea surface increase the complexity of the background, which seriously hinders the detection of targets. To achieve the detection of dark-polarity ship targets in such environments, a dual-channel threshold segmentation method based on local low-gray region detection and geometric features judgment is proposed in this paper. In one channel, adaptive threshold segmentation is performed on the low-gray regions of the acquired image and combined with geometric features to obtain a finer segmentation result. In the other channel, adaptive segmentation is performed on the preprocessed image, and potential backgrounds that may be finely segmented as targets are filtered out based on an area threshold. Finally, the results of the two channels are multiplied and fused to obtain an accurate segmentation result. Experimental results demonstrate that the proposed algorithm outperforms the comparison algorithm in subjective and objective evaluations. The proposed algorithm in this paper not only achieves a low false alarm rate but also exhibits a higher detection rate, and the average detection rate in the test sequence surpasses 95%. |
| first_indexed | 2024-03-09T17:03:52Z |
| format | Article |
| id | doaj.art-c4ec9f29a8dd4b739d03d4568de89078 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T17:03:52Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c4ec9f29a8dd4b739d03d4568de890782023-11-24T14:26:50ZengMDPI AGApplied Sciences2076-34172023-11-0113221224710.3390/app132212247Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple FeaturesDongming Lu0Jiangyun Tan1Mengke Wang2Longyin Teng3Liping Wang4Guohua Gu5School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, ChinaIn infrared images of the sea surface, apart from the complex background of the sea surface, there are often sky and island backgrounds. The disturbances caused by sea wind and the reflection of intense sunlight on the sea surface increase the complexity of the background, which seriously hinders the detection of targets. To achieve the detection of dark-polarity ship targets in such environments, a dual-channel threshold segmentation method based on local low-gray region detection and geometric features judgment is proposed in this paper. In one channel, adaptive threshold segmentation is performed on the low-gray regions of the acquired image and combined with geometric features to obtain a finer segmentation result. In the other channel, adaptive segmentation is performed on the preprocessed image, and potential backgrounds that may be finely segmented as targets are filtered out based on an area threshold. Finally, the results of the two channels are multiplied and fused to obtain an accurate segmentation result. Experimental results demonstrate that the proposed algorithm outperforms the comparison algorithm in subjective and objective evaluations. The proposed algorithm in this paper not only achieves a low false alarm rate but also exhibits a higher detection rate, and the average detection rate in the test sequence surpasses 95%.https://www.mdpi.com/2076-3417/13/22/12247complex sea backgroundinfrared ship detectionlow grayscale region detectiondual-channel segment |
| spellingShingle | Dongming Lu Jiangyun Tan Mengke Wang Longyin Teng Liping Wang Guohua Gu Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features Applied Sciences complex sea background infrared ship detection low grayscale region detection dual-channel segment |
| title | Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features |
| title_full | Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features |
| title_fullStr | Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features |
| title_full_unstemmed | Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features |
| title_short | Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features |
| title_sort | infrared ship target detection based on dual channel segmentation combined with multiple features |
| topic | complex sea background infrared ship detection low grayscale region detection dual-channel segment |
| url | https://www.mdpi.com/2076-3417/13/22/12247 |
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