Top

Arabian Journal for Science and Engineering

Published in:

24-09-2021 | Research Article-Computer Engineering and Computer Science

IARet: A Lightweight Multiscale Infrared Aerocraft Recognition Algorithm

Authors: Xinhao Jiang, Wei Cai, Zhiyong Yang, Peiwei Xu, Bo Jiang

Published in: Arabian Journal for Science and Engineering | Issue 2/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Detecting low-altitude unmanned aerial vehicle (UAV) has great significance in both military and civil fields. Aiming at the problem that it is difficult to detect infrared long-range aerocraft targets in complex background accurately and quickly, we propose a lightweight multiscale infrared aerocraft recognition algorithm called IARet. Firstly, in order to improve the detection speed, we use the Focus module at the input to greatly shorten the overall inference time. Secondly, in order to enhance the detection ability, we design a multistage Feature Extraction Part to take into account both lightweight and feature extraction ability, use the Path Aggregation Network to fuse multiscale features, and add an improved Receptive Field Block at the output. Comparison experiments on the Infrared Aerocraft Dataset show that, compared with the newly published high-performance detection model YOLOv5s, the recall of IARet is improved by 2%, the mAP@0.5 is improved by 1.6%, and the CPU inference speed is improved by 37.1%. So IARet model can consider both accuracy and speed. The calculation and parameters of the model are obviously reduced, and the model size is compressed to 4.8 MB, all of which reduce the dependence on the computing capability of hardware platform, and realize the accurate and fast detection of small aerocraft targets in the infrared band.

previous article Augmenting Driver’s Situational Awareness using Smartphones in VANETs

next article ILCAN: A New Vision Attention-Based Late Blight Disease Localization and Classification

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Wang, Z.; Yang, M.: Research on countermeasures against unmanned flying in large-scale activity airspace. In: The International Conference on Cyber Security Intelligence and Analytics, pp. 667–674. Springer, Cham (2021)CrossRef

Chen, Y.; Zhang, G.; Ma, Y.; Kang, J.U.; Kwan, C.: Small infrared target detection based on fast adaptive masking and scaling with iterative segmentation. IEEE Geoscience and Remote Sensing Letters (2021). https://doi.org/10.1109/LGRS.2020.3047524CrossRef

Rawat, S.S.; Verma, S.K.; Kumar, Y.: Review on recent development in infrared small target detection algorithms. Procedia Comput. Sci. 167, 2496–2505 (2020). https://doi.org/10.1016/j.procs.2020.03.302CrossRef

Simonyan, K.; Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). CoRR, abs/1409.1556. https://arxiv.org/abs/1409.1556

Ding, Y.; Zhao, X.F.; Zhang, Z.L.; Cai, W.; Yang, N.J.: Graph sample and aggregate-attention network for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. (2021). https://doi.org/10.1109/LGRS.2021.3062944CrossRef

Taha, M.A.; Şah, M.; Direkoğlu, C.: Review of place recognition approaches: traditional and deep learning methods. In: International Conference on Theory and Applications of Fuzzy Systems and Soft Computing, pp. 183–191. Springer, Cham (2020)

Girshick, R.; Donahue, J.; Darrell, T.; Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 580–587. (2013). https://arxiv.org/abs/1311.2524v4

Redmon, J.; Divvala, S.; Girshick, R.; Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788. (2016). https://doi.org/10.1109/CVPR.2016.91

Lin, T. Y.; Goyal, P.; Girshick, R.; He, K.; P Dollár.: Focal loss for dense object detection. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, PP(99), 2999–3007. (2017). https://doi.org/10.1109/ICCV.2017.324

10.

Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.E.; Fu, C.; Berg, A.: SSD: Single shot multibox detector. In: Computer Vision—ECCV 2016, pp. 21–37. (2016). https://doi.org/10.1007/978-3-319-46448-0_2

11.

Miao, Z.; Zhang, Y.; Chen, R. M.; Li, W. H.: Method for fast detection of infrared targets based on key points. Acta Optica Sinica, 40(23), 2312006. (2020). http://www.opticsjournal.net/Articles/Abstract/gxxb/40/23/2312006.cshtml

12.

Ding, L.; Xu, X.; Cao, Y.; Zhai, G.; Qian, L.: Detection and tracking of infrared small target by jointly using SSD and pipeline filter. Digit. Signal Process. 110, 102949 (2020). https://doi.org/10.1016/j.dsp.2020.102949CrossRef

13.

Ju, M.; Luo, J.; Liu, G.; Luo, H.: ISTDet: An efficient end-to-end neural network for infrared small target detection. Infrar. Phys. Technol. 114(7), 103659 (2021). https://doi.org/10.1016/j.infrared.2021.103659CrossRef

14.

Ding, Y.; Zhao, X. F.; Zhang, Z. L.; Cai, W.; Yang, N. J, Zhan, Y.: Semi-supervised locality preserving dense graph neural network with arma filters and context-aware learning for hyperspectral image classification. In: IEEE Geoscience and Remote Sensing Letters, (2021). https://ieeexplore.ieee.org/document/9506991

15.

Zhang, H.; Sun, M.; Li, Q.; Liu, L.; Liu, M.; Ji, Y.: An empirical study of multi-scale object detection in high resolution UAV images. Neurocomputing 421, 173–182 (2021). https://doi.org/10.1016/j.neucom.2020.08.074CrossRef

16.

Dong, L.; Zhang, H.J.; Ji, Y.Z.; Ding, Y.X.: Crowd counting by using multi-level density-based spatial information: A multi-scale CNN framework. Inf. Sci. 528, 79–91 (2020). https://doi.org/10.1016/j.ins.2020.04.001MathSciNetCrossRef

17.

Chen, Y. K.; Li, Y. W.; Kong, T.; Qi, L.; Chu, R. H.; Li, L.; Jia, J. Y.: Scale-aware automatic augmentation for object detection. ArXiv, abs/2103.17220. (2021). https://arxiv.org/abs/2103.17220

18.

yolov5: v3.1—Bug Fixes and Performance Improvements (Version v3.1) [OL]. (2020-10-29)[2021-03-25]. https://doi.org/10.5281/zenodo.4154370.

19.

Gao, S.; Cheng, M.M.; Zhao, K.; Zhang, X.Y.; Torr, P.: Res2net: a new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652–662 (2019). https://doi.org/10.1109/TPAMI.2019.2938758CrossRef

20.

Liu, S.; Qi, L.; Qin, H.; Shi, J.; Jia, J.: Path aggregation network for instance segmentation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8759–8768, (2018). https://doi.org/10.1109/CVPR.2018.00913

21.

Liu, S. T.; Huang, D.; Wang, Y. H.: Receptive field block net for accurate and fast object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 385–400, (2018). https://doi.org/10.1007/978-3-030-01252-6_24

22.

Redmon, J.; A. Farhadi.: YOLO9000: Better, Faster, Stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525, (2017). https://doi.org/10.1109/CVPR.2017.690

23.

Hou, Q.; Zhou, D.; Feng, J.: Coordinate attention for efficient mobile network design. ArXiv, abs/2103.02907. (2021). https://arxiv.org/abs/2103.02907

24.

Peng, H.; Sun, H.; Guo, Y.: 3D multi-scale deep convolutional neural networks for pulmonary nodule detection. Plos one 16(1), 1–14 (2021). https://doi.org/10.1371/journal.pone.0244406CrossRef

25.

Zheng, Z.; Wang, P.; Liu, W.; Li, J.; Ye, R.; Ren, D.: Distance-IoU loss: faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence 34(07), 12993–13000 (2020). https://doi.org/10.1609/aaai.v34i07.6999CrossRef

26.

Hui, B. W.; Song, Z. Y.; Fan, H. Q.; Zhong, P.; Hu, W. D.; Zhang, X. F.; Ling, J. G.; Su, H. Y.; Jin, W.; Zhang, Y. J.; Bai, Y. Q.: A dataset for infrared image dim-small aerocraft target detection and tracking under ground / air background [DB/OL]. Science Data Bank, 2019. (2019–10–28) [2021–03–25]. https://doi.org/10.11922/sciencedb.902

27.

Far from the sea (10–12km) ship target detection dataset. [OL]. (2020–05–22)[2021–03–25]. http://www.gxzx.sdu.edu.cn/info/1133/2174.htm

28.

FLIR Thermal Dataset. [OL]. (2019–06–18)[2021–03–25]. https://www.flir.cn/oem/adas/adas-dataset-form/

29.

Tan, M. X.; Pang, R. M.; Le, Q. V.: EfficientDet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10781-10790, (2020).https://doi.org/10.1109/CVPR42600.2020.01079

30.

Zhou, X.; Wang, D.; Krähenbühl, P.: Objects as points ArXiv, abs/1904.07850, (2019). https://arxiv.org/abs/1904.07850

Title: IARet: A Lightweight Multiscale Infrared Aerocraft Recognition Algorithm
Authors: Xinhao Jiang
Wei Cai
Zhiyong Yang
Peiwei Xu
Bo Jiang
Publication date: 24-09-2021
Publisher: Springer Berlin Heidelberg
Published in: Arabian Journal for Science and Engineering / Issue 2/2022
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-021-06181-7

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 2/2022

RFID in IoT, Miniaturized Pentagonal Slot-based Data Dense Chipless RFID Tag for IoT Applications

Empirical Analysis of Machine Learning Algorithms on Imbalance Electrocardiogram Based Arrhythmia Dataset for Heart Disease Detection

Detection of Turkish Fake News in Twitter with Machine Learning Algorithms

A Model-Driven Framework for the Development of MVC-Based (Web) Application

Load Balancing in DCN Servers through SDN Machine Learning Algorithm

Conditional Deep Convolutional Generative Adversarial Networks for Isolated Handwritten Arabic Character Generation

Premium Partners