场景抽象语义综合模型及其在红外弱小目标检测中的应用
收稿日期: 2024-05-20
修回日期: 2024-06-11
录用日期: 2024-06-28
网络出版日期: 2024-07-01
基金资助
国家自然科学基金(62273279);国家资助博士后研究人员计划(GZC20232105);西北工业大学博士论文创新基金(CX2024043)
Scene abstract semantic synthesis model and its application in infrared dim and small target detection
Received date: 2024-05-20
Revised date: 2024-06-11
Accepted date: 2024-06-28
Online published: 2024-07-01
Supported by
National Natural Science Foundation of China(62273279);Postdoctoral Fellowship Program of CPSF(GZC20232105);Sponsored by Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University(CX2024043)
多任务、跨域复杂环境下弱小目标准确、稳定检测在红外预警、搜索与跟踪、精确制导等领域具有重要作用。针对当前算法过于依赖人工设计策略与先验知识，以提取目标信息为目的，对于场景信息的挖掘和利用不充分，性能受限且环境适应性不足等问题，提出了基于场景抽象语义综合的弱小目标检测方法。首先，利用交叉注意力、孪生网络、扩展语义图和自学习双通道的方法设计了4种场景抽象语义综合模型。接着，基于4种语义综合模型设计了基于场景语义综合的弱小目标检测网络，将场景类别语义信息引入到红外弱小目标检测过程实现多类复杂背景下红外弱小目标检测。最后，实验结果表明提出的基于自学习双通道语义综合的弱小目标检测算法精确度到达84.24%，召回率达到89.68%。
李少毅 , 张雅淇 , 程岳 , 杨曦 , 张良 , 林健 , 孟中杰 . 场景抽象语义综合模型及其在红外弱小目标检测中的应用[J]. 航空学报, 2024 , 45(20) : 630702 -630702 . DOI: 10.7527/S1000-6893.2024.30702
Accurate and stable detection of dim and small targets in complex， multi-task， and cross-domain environments plays a critical role in infrared early warning， search and tracking， and precision guidance. Current algorithms overly rely on manually designed strategies and prior knowledge， focusing primarily on target information extraction while insufficiently mining and utilizing scene information. This results in limited performance and inadequate environmental adaptability. To address these issues， this paper proposes a dim and small target detection method based on scene abstract semantic synthesis. First， four scene abstract semantic synthesis models are designed using cross-attention， Siamese networks， extended semantic graphs， and self-learning dual-channel methods. Next， based on these four semantic synthesis models， a dim and small target detection network based on scene semantic synthesis is designed. This network incorporates scene category semantic information into the infrared dim and small target detection process to achieve detection in various complex backgrounds. Finally， experimental results show that the proposed self-learning dual-channel semantic synthesis-based dim and small target detection algorithm achieves an accuracy of 84.24% and a recall rate of 89.68%.
| 1 | AGHAZIYARATI S， MORADI S， TALEBI H. Small infrared target detection using absolute average difference weighted by cumulative directional derivatives［J］. Infrared Physics and Technology， 2019， 101： 78-87. |
| 2 | MORADI S， MOALLEM P， SABAHI M F. Fast and robust small infrared target detection using absolute directional mean difference algorithm［J］. Signal Processing， 2020， 177： 107727. |
| 3 | PHILIP CHEN C L， LI H， WEI Y T， et al. A local contrast method for small infrared target detection［J］. IEEE Transactions on Geoscience and Remote Sensing， 2014， 52（1）： 574-581. |
| 4 | HAN J H， MA Y， ZHOU B， et al. A robust infrared small target detection algorithm based on human visual system［J］. IEEE Geoscience and Remote Sensing Letters， 2014， 11（12）： 2168-2172. |
| 5 | WANG H， ZHOU L P， WANG L. Miss detection vs. false alarm： Adversarial learning for small object segmentation in infrared images［C］∥2019 IEEE/CVF International Conference on Computer Vision （ICCV）. Piscataway： IEEE Press， 2019： 8508-8517. |
| 6 | ZHAO M X， CHENG L， YANG X， et al. TBC-Net： A real-time detector for infrared small target detection using semantic constraint［DB/OL］. arXiv preprint： 2001.05852， 2019. |
| 7 | DAI Y M， WU Y Q， ZHOU F， et al. Attentional local contrast networks for infrared small target detection［J］. IEEE Transactions on Geoscience and Remote Sensing， 2021， 59（11）： 9813-9824. |
| 8 | LI H Q， YANG J F， WANG R S， et al. ILNet： Low-level matters for salient infrared small target detection［DB/OL］. arXiv preprint： 2309.13646， 2023. |
| 9 | ZHANG T F， LI L， CAO S Y， et al. Attention-guided pyramid context networks for detecting infrared small target under complex background［J］. IEEE Transactions on Aerospace and Electronic Systems， 2023， 59（4）： 4250-4261. |
| 10 | LI B Y， XIAO C， WANG L G， et al. Dense nested attention network for infrared small target detection［J］. IEEE Transactions on Image Processing： A Publication of the IEEE Signal Processing Society， 2023， 32： 1745-1758. |
| 11 | WANG C Y， WANG H， PAN P W. Local contrast and global contextual information make infrared small object salient again［DB/OL］. arXiv preprint： 2301.12093， 2023. |
| 12 | LIN J， LI S Y， ZHANG L， et al. IR-TransDet： Infrared dim and small target detection with IR-transformer［J］. IEEE Transactions on Geoscience and Remote Sensing， 2023， 61： 5004813. |
| 13 | 王潇， 刘贞报. 基于多层多向Transformer的红外弱小目标检测［J］. 航空学报， 2024， 45（14）： 629490. |
| XU X， LIU Z B. Infrared small target detection based on multi-layers multi-directions Transformer［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（14）： 629490 （in Chinese）. | |
| 14 | 王强， 吴乐天， 王勇， 等. 基于关键点检测的红外弱小目标检测［J］. 航空学报， 2023， 44（10）： 328173. |
| WANG Q， WU L T， WANG Y， et al. An infrared small target detection method based on key point［J］. Acta Aeronautica et Astronautica Sinica： 2023， 44（10）：328173 （in Chinese）. | |
| 15 | CHEN C Y， GONG W G， CHEN Y L， et al. Object detection in remote sensing images based on a scene-contextual feature pyramid network［J］. Remote Sensing， 2019， 11（3）： 339. |
| 16 | MOTTAGHI R， CHEN X J， LIU X B， et al. The role of context for object detection and semantic segmentation in the wild［C］∥2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE Press， 2014： 891-898. |
| 17 | JING D， ZHANG S， CONG R M， et al. Occlusion-aware Bi-directional guided network for light field salient object detection［C］∥Proceedings of the 29th ACM International Conference on Multimedia. New York： ACM， 2021： 1692-1701. |
| 18 | TIAN X Q， LI S Y， YANG X， et al. Joint spatio-temporal features and sea background prior for infrared dim and small target detection［J］. Infrared Physics & Technology， 2023， 130： 104612. |
| 19 | ZUO Z， TONG X Z， WEI J Y， et al. AFFPN： Attention fusion feature pyramid network for small infrared target detection［J］. Remote Sensing， 2022， 14（14）： 3412. |
| 20 | YAO J P， XIAO S Z， DENG Q Q， et al. An infrared maritime small target detection algorithm based on semantic， detail， and edge multidimensional information fusion［J］. Remote Sensing， 2023， 15（20）： 4909. |
| 21 | YU J M， LI S， ZHOU S B， et al. MSIA-net： A lightweight infrared target detection network with efficient information fusion［J］. Entropy， 2023， 25（5）： 808. |
| 22 | TORRALBA A， OLIVA A， CASTELHANO M S， et al. Contextual guidance of eye movements and attention in real-world scenes： The role of global features in object search［J］. Psychological Review， 2006， 113（4）： 766-786. |
| 23 | LONG J， SHELHAMER E， DARRELL T. Fully convolutional networks for semantic segmentation［C］∥2015 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2015： 3431-3440. |
| 24 | RAHMAN M A， WANG Y. Optimizing intersection-over-union in deep neural networks for image segmentation［C］∥International Symposium on Visual Computing. Cham： Springer， 2016： 234-244. |
| 25 | PENG J C， LIU Y， TANG S Y， et al. PP-LiteSeg： A superior real-time semantic segmentation model［DB/OL］. arXiv preprint： 2204.02681， 2022. |
| 26 | LIN T Y， GOYAL P， GIRSHICK R， et al. Focal loss for dense object detection［C］∥2017 IEEE International Conference on Computer Vision （ICCV）. Piscataway： IEEE Press， 2017： 2999-3007. |
| 27 | HUI B， SONG Z， FAN H， et al. A dataset for infrared image dim-small aircraft target detection and tracking under ground/air background［J］. Science Data Bank， 2019， 5（12）： 4. |
| 28 | RONNEBERGER O， FISCHER P， BROX T. U-net： Convolutional networks for biomedical image segmentation［C］∥International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham： Springer， 2015： 234-241. |
| 29 | DAI Y M， WU Y Q， ZHOU F， et al. Asymmetric contextual modulation for infrared small target detection［C］∥2021 IEEE Winter Conference on Applications of Computer Vision （WACV）. Piscataway： IEEE Press， 2021： 949-958. |
| 30 | LIU Z， LIN Y T， CAO Y， et al. Swin transformer： Hierarchical vision transformer using shifted windows［C］∥2021 IEEE/CVF International Conference on Computer Vision （ICCV）. Piscataway： IEEE Press， 2021： 9992-10002. |
| 31 | DESHPANDE S D， ER M H， VENKATESWARLU R， et al. Max-mean and max-median filters for detection of small targets［C］∥SPIE Proceedings"， "Signal and Data Processing of Small Targets 1999， 1999： 74-83. |
| 32 | WEI Y T， YOU X G， LI H. Multiscale patch-based contrast measure for small infrared target detection［J］. Pattern Recognition， 2016， 58： 216-226. |
| 33 | HAN J H， LIANG K， ZHOU B， et al. Infrared small target detection utilizing the multiscale relative local contrast measure［J］. IEEE Geoscience and Remote Sensing Letters， 2018， 15（4）： 612-616. |
| 34 | HAN J H， MORADI S， FARAMARZI I， et al. A local contrast method for infrared small-target detection utilizing a tri-layer window［J］. IEEE Geoscience and Remote Sensing Letters， 2020， 17（10）： 1822-1826. |
| 35 | SHI Y F， WEI Y T， YAO H， et al. High-boost-based multiscale local contrast measure for infrared small target detection［J］. IEEE Geoscience and Remote Sensing Letters， 2018， 15（1）： 33-37. |
| 36 | XIA C Q， LI X R， ZHAO L Y， et al. Infrared small target detection based on multiscale local contrast measure using local energy factor［J］. IEEE Geoscience and Remote Sensing Letters， 2020， 17（1）： 157-161. |
| 37 | HAN J H， MORADI S， FARAMARZI I， et al. Infrared small target detection based on the weighted strengthened local contrast measure［J］. IEEE Geoscience and Remote Sensing Letters， 2021， 18（9）： 1670-1674. |
| 38 | ZHANG F， LI C F， SHI L N. Detecting and tracking dim moving point target in IR image sequence［J］. Infrared Physics & Technology， 2005， 46（4）： 323-328. |
| 39 | ZHANG H， ZHANG L， YUAN D， et al. Infrared small target detection based on local intensity and gradient properties［J］. Infrared Physics and Technology， 2018， 89： 88-96. |
/
| 〈 |
|
〉 |
CJA