Infrared Small UAV Target Detection Algorithm Based on Enhanced Adaptive Feature Pyramid Networks
Under the explosive growth of “low-slow-small” unmanned aerial vehicles (UAVs), harassment incidents with “low-slow-small” UAVs, such as illegal surveying and mapping, no-fly zone flights, terrorist attacks, have frequently been occurring due to the low en...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9936666/ |
| _version_ | 1797985063210582016 |
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| author | Zheng Lu Peng Yueping Ye Zecong Jiang Rongqi Zhou Tongtong |
| author_facet | Zheng Lu Peng Yueping Ye Zecong Jiang Rongqi Zhou Tongtong |
| author_sort | Zheng Lu |
| collection | DOAJ |
| description | Under the explosive growth of “low-slow-small” unmanned aerial vehicles (UAVs), harassment incidents with “low-slow-small” UAVs, such as illegal surveying and mapping, no-fly zone flights, terrorist attacks, have frequently been occurring due to the low entry barrier to the industry and the lack of market norms and control standards, and severely impair the safety of public life and property. The premise of developing anti-UAV technology is efficient and accurate UAV target detection, which plays a vital role in the follow-up UAV target tracking, attack, and interception. This paper proposes a new DEAX algorithm named enhanced adaptive feature pyramid networks-based Target Detection Algorithm for infrared small UAV target detection. Our proposed algorithm improves the original feature pyramid networks in four aspects for the small UAV target detection task. 1) Use channel separation to keep more channels when adjusting the number of channels in convolutional to avoid the loss of helpful feature information; 2) Design a feature enhancement module to enhance “target feature” and suppress “non-target feature”; 3) Alleviate the differences of receptive fields and semantic information among different layers by shared convolution; 4) Introduce adaptive feature fusion method into feature fusion and complete the construction of enhanced adaptive feature pyramid networks (EAFPN) to solve the problem of weakening feature expression in cross-scale feature fusion. EAFPN is applied to the single-stage target detection networks. We find the detection accuracy and speed of the algorithm outperform those based on feature pyramid networks (FPN), where the improvement on mAP is 8.9%. |
| first_indexed | 2024-04-11T07:12:39Z |
| format | Article |
| id | doaj.art-cae0c02bcbe4484da9f3e85c360ca8b0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T07:12:39Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cae0c02bcbe4484da9f3e85c360ca8b02022-12-22T04:38:10ZengIEEEIEEE Access2169-35362022-01-011011598811599510.1109/ACCESS.2022.32190899936666Infrared Small UAV Target Detection Algorithm Based on Enhanced Adaptive Feature Pyramid NetworksZheng Lu0Peng Yueping1https://orcid.org/0000-0003-1371-0231Ye Zecong2Jiang Rongqi3Zhou Tongtong4Postgraduate Brigade, Engineering University of PAP, Xi’an, ChinaCollege of Information Engineering, Engineering University of PAP, Xi’an, ChinaPostgraduate Brigade, Engineering University of PAP, Xi’an, ChinaPostgraduate Brigade, Engineering University of PAP, Xi’an, ChinaPostgraduate Brigade, Engineering University of PAP, Xi’an, ChinaUnder the explosive growth of “low-slow-small” unmanned aerial vehicles (UAVs), harassment incidents with “low-slow-small” UAVs, such as illegal surveying and mapping, no-fly zone flights, terrorist attacks, have frequently been occurring due to the low entry barrier to the industry and the lack of market norms and control standards, and severely impair the safety of public life and property. The premise of developing anti-UAV technology is efficient and accurate UAV target detection, which plays a vital role in the follow-up UAV target tracking, attack, and interception. This paper proposes a new DEAX algorithm named enhanced adaptive feature pyramid networks-based Target Detection Algorithm for infrared small UAV target detection. Our proposed algorithm improves the original feature pyramid networks in four aspects for the small UAV target detection task. 1) Use channel separation to keep more channels when adjusting the number of channels in convolutional to avoid the loss of helpful feature information; 2) Design a feature enhancement module to enhance “target feature” and suppress “non-target feature”; 3) Alleviate the differences of receptive fields and semantic information among different layers by shared convolution; 4) Introduce adaptive feature fusion method into feature fusion and complete the construction of enhanced adaptive feature pyramid networks (EAFPN) to solve the problem of weakening feature expression in cross-scale feature fusion. EAFPN is applied to the single-stage target detection networks. We find the detection accuracy and speed of the algorithm outperform those based on feature pyramid networks (FPN), where the improvement on mAP is 8.9%.https://ieeexplore.ieee.org/document/9936666/Object detectionfeature pyramidfeature enhancement |
| spellingShingle | Zheng Lu Peng Yueping Ye Zecong Jiang Rongqi Zhou Tongtong Infrared Small UAV Target Detection Algorithm Based on Enhanced Adaptive Feature Pyramid Networks IEEE Access Object detection feature pyramid feature enhancement |
| title | Infrared Small UAV Target Detection Algorithm Based on Enhanced Adaptive Feature Pyramid Networks |
| title_full | Infrared Small UAV Target Detection Algorithm Based on Enhanced Adaptive Feature Pyramid Networks |
| title_fullStr | Infrared Small UAV Target Detection Algorithm Based on Enhanced Adaptive Feature Pyramid Networks |
| title_full_unstemmed | Infrared Small UAV Target Detection Algorithm Based on Enhanced Adaptive Feature Pyramid Networks |
| title_short | Infrared Small UAV Target Detection Algorithm Based on Enhanced Adaptive Feature Pyramid Networks |
| title_sort | infrared small uav target detection algorithm based on enhanced adaptive feature pyramid networks |
| topic | Object detection feature pyramid feature enhancement |
| url | https://ieeexplore.ieee.org/document/9936666/ |
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