Infrared UAV Target Detection Based on Continuous-Coupled Neural Network
The task of the detection of unmanned aerial vehicles (UAVs) is of great significance to social communication security. Infrared detection technology has the advantage of not being interfered with by environmental and other factors and can detect UAVs in complex environments. Since infrared detectio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/14/11/2113 |
| _version_ | 1797458396345008128 |
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| author | Zhuoran Yang Jing Lian Jizhao Liu |
| author_facet | Zhuoran Yang Jing Lian Jizhao Liu |
| author_sort | Zhuoran Yang |
| collection | DOAJ |
| description | The task of the detection of unmanned aerial vehicles (UAVs) is of great significance to social communication security. Infrared detection technology has the advantage of not being interfered with by environmental and other factors and can detect UAVs in complex environments. Since infrared detection equipment is expensive and data collection is difficult, there are few existing UAV-based infrared images, making it difficult to train deep neural networks; in addition, there are background clutter and noise in infrared images, such as heavy clouds, buildings, etc. The signal-to-clutter ratio is low, and the signal-to-noise ratio is low. Therefore, it is difficult to achieve the UAV detection task using traditional methods. The above challenges make infrared UAV detection a difficult task. In order to solve the above problems, this work drew upon the visual processing mechanism of the human brain to propose an effective framework for UAV detection in infrared images. The framework first determines the relevant parameters of the continuous-coupled neural network (CCNN) through the image’s standard deviation, mean, etc. Then, it inputs the image into the CCNN, groups the pixels through iteration, then obtains the segmentation result through expansion and erosion, and finally, obtains the final result through the minimum circumscribed rectangle. The experimental results showed that, compared with the existing most-advanced brain-inspired image-understanding methods, this framework has the best intersection over union (IoU) (the intersection over union is the overlapping area between the predicted segmentation and the label divided by the joint area between the predicted segmentation and the label) in UAV infrared images, with an average of 74.79% (up to 97.01%), and can effectively realize the task of UAV detection. |
| first_indexed | 2024-03-09T16:36:31Z |
| format | Article |
| id | doaj.art-b4b773a4f1af487abc5fb6dd62a74cec |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-09T16:36:31Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-b4b773a4f1af487abc5fb6dd62a74cec2023-11-24T14:56:35ZengMDPI AGMicromachines2072-666X2023-11-011411211310.3390/mi14112113Infrared UAV Target Detection Based on Continuous-Coupled Neural NetworkZhuoran Yang0Jing Lian1Jizhao Liu2School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaThe task of the detection of unmanned aerial vehicles (UAVs) is of great significance to social communication security. Infrared detection technology has the advantage of not being interfered with by environmental and other factors and can detect UAVs in complex environments. Since infrared detection equipment is expensive and data collection is difficult, there are few existing UAV-based infrared images, making it difficult to train deep neural networks; in addition, there are background clutter and noise in infrared images, such as heavy clouds, buildings, etc. The signal-to-clutter ratio is low, and the signal-to-noise ratio is low. Therefore, it is difficult to achieve the UAV detection task using traditional methods. The above challenges make infrared UAV detection a difficult task. In order to solve the above problems, this work drew upon the visual processing mechanism of the human brain to propose an effective framework for UAV detection in infrared images. The framework first determines the relevant parameters of the continuous-coupled neural network (CCNN) through the image’s standard deviation, mean, etc. Then, it inputs the image into the CCNN, groups the pixels through iteration, then obtains the segmentation result through expansion and erosion, and finally, obtains the final result through the minimum circumscribed rectangle. The experimental results showed that, compared with the existing most-advanced brain-inspired image-understanding methods, this framework has the best intersection over union (IoU) (the intersection over union is the overlapping area between the predicted segmentation and the label divided by the joint area between the predicted segmentation and the label) in UAV infrared images, with an average of 74.79% (up to 97.01%), and can effectively realize the task of UAV detection.https://www.mdpi.com/2072-666X/14/11/2113CCNNinfrared image processingUAV detection |
| spellingShingle | Zhuoran Yang Jing Lian Jizhao Liu Infrared UAV Target Detection Based on Continuous-Coupled Neural Network Micromachines CCNN infrared image processing UAV detection |
| title | Infrared UAV Target Detection Based on Continuous-Coupled Neural Network |
| title_full | Infrared UAV Target Detection Based on Continuous-Coupled Neural Network |
| title_fullStr | Infrared UAV Target Detection Based on Continuous-Coupled Neural Network |
| title_full_unstemmed | Infrared UAV Target Detection Based on Continuous-Coupled Neural Network |
| title_short | Infrared UAV Target Detection Based on Continuous-Coupled Neural Network |
| title_sort | infrared uav target detection based on continuous coupled neural network |
| topic | CCNN infrared image processing UAV detection |
| url | https://www.mdpi.com/2072-666X/14/11/2113 |
| work_keys_str_mv | AT zhuoranyang infrareduavtargetdetectionbasedoncontinuouscoupledneuralnetwork AT jinglian infrareduavtargetdetectionbasedoncontinuouscoupledneuralnetwork AT jizhaoliu infrareduavtargetdetectionbasedoncontinuouscoupledneuralnetwork |