Ground-Target Recognition Method Based on Transfer Learning
A moving ground-target recognition system can monitor suspicious activities of pedestrians and vehicles in key areas. Currently, most target recognition systems are based on devices such as fiber optics, radar, and vibration sensors. A system based on vibration sensors has the advantages of small si...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/2/576 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587530897195008 |
|---|---|
| author | Qiuzhan Zhou Jikang Hu Huinan Wu Cong Wang Pingping Liu Xinyi Yao |
| author_facet | Qiuzhan Zhou Jikang Hu Huinan Wu Cong Wang Pingping Liu Xinyi Yao |
| author_sort | Qiuzhan Zhou |
| collection | DOAJ |
| description | A moving ground-target recognition system can monitor suspicious activities of pedestrians and vehicles in key areas. Currently, most target recognition systems are based on devices such as fiber optics, radar, and vibration sensors. A system based on vibration sensors has the advantages of small size, low power consumption, strong concealment, easy installation, and low power consumption. However, existing recognition algorithms generally suffer from problems such as the inability to recognize long-distance moving targets and adapt to new environments, as well as low recognition accuracy. Here, we demonstrate that applying transfer learning to recognition algorithms can adapt to new environments and improve accuracy. We proposed a new moving ground-target recognition algorithm based on CNN and domain adaptation. We used convolutional neural networks (CNNS) to extract depth features from target vibration signals to identify target types. We used transfer learning to make the algorithm more adaptable to environmental changes. Our results show that the proposed moving ground-target recognition algorithm can identify target types, improve accuracy, and adapt to a new environment with good performance. We anticipate that our algorithm will be the starting point for more complex recognition algorithms. For example, target recognition algorithms based on multi-modal fusion and transfer learning can better meet actual needs. |
| format | Article |
| id | doaj-art-d7ac1d1a3832455eac32e5f87eaf2205 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d7ac1d1a3832455eac32e5f87eaf22052025-01-24T13:49:24ZengMDPI AGSensors1424-82202025-01-0125257610.3390/s25020576Ground-Target Recognition Method Based on Transfer LearningQiuzhan Zhou0Jikang Hu1Huinan Wu2Cong Wang3Pingping Liu4Xinyi Yao5College of Communication Engineering, Jilin University, Changchun 130012, ChinaCollege of Communication Engineering, Jilin University, Changchun 130012, ChinaCollege of Communication Engineering, Jilin University, Changchun 130012, ChinaCollege of Communication Engineering, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Communication Engineering, Jilin University, Changchun 130012, ChinaA moving ground-target recognition system can monitor suspicious activities of pedestrians and vehicles in key areas. Currently, most target recognition systems are based on devices such as fiber optics, radar, and vibration sensors. A system based on vibration sensors has the advantages of small size, low power consumption, strong concealment, easy installation, and low power consumption. However, existing recognition algorithms generally suffer from problems such as the inability to recognize long-distance moving targets and adapt to new environments, as well as low recognition accuracy. Here, we demonstrate that applying transfer learning to recognition algorithms can adapt to new environments and improve accuracy. We proposed a new moving ground-target recognition algorithm based on CNN and domain adaptation. We used convolutional neural networks (CNNS) to extract depth features from target vibration signals to identify target types. We used transfer learning to make the algorithm more adaptable to environmental changes. Our results show that the proposed moving ground-target recognition algorithm can identify target types, improve accuracy, and adapt to a new environment with good performance. We anticipate that our algorithm will be the starting point for more complex recognition algorithms. For example, target recognition algorithms based on multi-modal fusion and transfer learning can better meet actual needs.https://www.mdpi.com/1424-8220/25/2/576target recognitionconvolutional neural networkdomain adaptationtransfer learningvibration sensors |
| spellingShingle | Qiuzhan Zhou Jikang Hu Huinan Wu Cong Wang Pingping Liu Xinyi Yao Ground-Target Recognition Method Based on Transfer Learning Sensors target recognition convolutional neural network domain adaptation transfer learning vibration sensors |
| title | Ground-Target Recognition Method Based on Transfer Learning |
| title_full | Ground-Target Recognition Method Based on Transfer Learning |
| title_fullStr | Ground-Target Recognition Method Based on Transfer Learning |
| title_full_unstemmed | Ground-Target Recognition Method Based on Transfer Learning |
| title_short | Ground-Target Recognition Method Based on Transfer Learning |
| title_sort | ground target recognition method based on transfer learning |
| topic | target recognition convolutional neural network domain adaptation transfer learning vibration sensors |
| url | https://www.mdpi.com/1424-8220/25/2/576 |
| work_keys_str_mv | AT qiuzhanzhou groundtargetrecognitionmethodbasedontransferlearning AT jikanghu groundtargetrecognitionmethodbasedontransferlearning AT huinanwu groundtargetrecognitionmethodbasedontransferlearning AT congwang groundtargetrecognitionmethodbasedontransferlearning AT pingpingliu groundtargetrecognitionmethodbasedontransferlearning AT xinyiyao groundtargetrecognitionmethodbasedontransferlearning |