Infrared target detection algorithm based on improved Faster R-CNN

WANG Xichen; PENG Fulun; LI Yexun; ZHANG Junju

doi:10.5768/JAO202445.0202001

Journal of Applied Optics > 2024 > 45(2): 346-353. > DOI: 10.5768/JAO202445.0202001

WANG Xichen, PENG Fulun, LI Yexun, ZHANG Junju. Infrared target detection algorithm based on improved Faster R-CNN[J]. Journal of Applied Optics, 2024, 45(2): 346-353. DOI: 10.5768/JAO202445.0202001

Citation:

PDF (2534 KB)

Infrared target detection algorithm based on improved Faster R-CNN

1.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2.
Xi'an Institute of Applied Optics, Xi'an 710065, China
3.
Jiangsu North Huguang Photoelectric Co.,Ltd., Wuxi 214100, China

More Information

Received Date: April 10, 2023
Revised Date: August 09, 2023
Available Online: February 04, 2024

Graphical Abstract

Abstract

Abstract

In order to improve the detection accuracy of infrared targets, a Faster R-CNN infrared target detection algorithm introducing a frequency domain attention mechanism was proposed. Firstly, a parallel image enhancement preprocessing structure was designed to address the issues of edge blur and noise in infrared images. Secondly, a frequency domain attention mechanism was introduced into Faster R-CNN, and a new infrared target detection backbone network was designed. Finally, a path enhanced pyramid structure was introduced to fuse multi-scale features for prediction, and the rich location information of the underlying network was utilized to improve detection accuracy. The experiment was conducted on a dataset of infrared aircraft. The results show that the AP of improved Faster R-CNN target detection framework is 7.6% higher than that of the algorithm with ResNet50 as the main stem. In addition, compared with current mainstream algorithms, the proposed algorithm improves the detection accuracy of infrared targets and verifies the effectiveness of the algorithm improvement.
- infrared target detection,
- image enhancement,
- Faster R-CNN,
- frequency domain attention mechanism,
- multi-scale feature fusion

FullText(HTML)

References (17)

References

[1]	曹红燕, 沈小林, 刘长明, 等. 改进的YOLOv3的红外目标检测算法[J]. 电子测量与仪器学报, 2020, 34(8): 188-194. CAO Hongyan, SHEN Xiaolin, LIU Changming, et al. Improved YOLOv3 infrared target detection algorithm [J]. Electronic Measurement and Instrument, 20, 34(8): 188-194.
[2]	顾佼佼, 李炳臻, 刘克, 等. 基于改进Faster R-CNN的红外舰船目标检测算法[J]. 红外技术,2021,43(2):170-178. GU Jiaojiao, LI Bingzhen, LIU Ke, et al. Infrared ship target detection algorithm based on improved Faster R-CNN[J]. Infrared Technology,2021,43(2):170-178.
[3]	蔡伟, 徐佩伟, 杨志勇, 等. 复杂背景下红外图像弱小目标检测[J]. 应用光学,2021,42(4):643-650. doi: 10.5768/JAO202142.0402002 CAI Wei, XU Peiwei, YANG Zhiyong, et al. Dim target detection in infrared image with complex background[J]. Applied Optics,2021,42(4):643-650. doi: 10.5768/JAO202142.0402002
[4]	谌海云, 余鸿皓, 王海川, 等. 基于改进YOLOX的红外目标检测算法[J]. 电子测量技术,2022,45(23):72-81. CHEN Haiyun, YU Honghao, WANG Haichuan, et al. Infrared target detection algorithm based on improved YOLOX[J]. Electronic Measurement Technology,2022,45(23):72-81.
[5]	GIRSHICK R. Faster R-CNN[C]//2015 IEEE International Conference on Computer Vision. New York: IEEE, 2015: 1440-1448.
[6]	HE K, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision. New York: IEEE, 2017: 2961-2969.
[7]	CAI Z, VASCONCELOS N. Cascade R-CNN: high quality object detection and instance segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2019,43(5):1483-1498.
[8]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 779-788.
[9]	REDMON J, FARHADI A. Yolo9000: better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2017: 7263-7271.
[10]	GE Z, LIU S, WANG F, et al. YOLOX: exceeding YOLO series in 2021[EB/OL]. [2023-03-20]. https://ui.adsabs.harvard.edu/abs/2021arXiv210708430G/abstract.
[11]	LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision. New York: IEEE, 2017: 2980-2988.
[12]	HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2018: 7132-7141.
[13]	QIN Z, ZHANG P, WU F, et al. Fcanet: frequency channel attention networks[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. New York: IEEE, 2021: 783-792.
[14]	LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2017: 2117-2125.
[15]	WANG K, LIU S, QI L, QIN H, et al. Path aggregation network for instance segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2018: 8759-8768.
[16]	回丙伟, 宋志勇, 范红旗, 等. 红外序列图像中弱小飞机目标检测跟踪数据集[DB/OL]. Science Data Bank, 2019 [2019-10-28]. http://10.11922/csdata.2019.0074.zh. HUI Bingwei, SONG Zhiyong, FAN Hongqi, et al. A dataset for dim-small target detection and tracking of aircraft in infrared image sequences [DB/OL]. Science Data Bank, 2019 [2019-10-28]. http://10.11922/csdata.2019.0074.zh.
[17]	LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft coco: common objects in context[C]//Proceedings of 13th European Conference on Computer Vision–ECCV 2014, Part V 13. Zurich, Switzerland: Springer International Publishing, 2014: 740-755.

Cited By

Cited by

Periodical cited type(4)

1.	侯军，杨洁，邵凯青. 基于YOLOv8的车载红外目标检测改进算法研究. 计量学报. 2025(02): 167-176 .
2.	张龙，吕鹏远，兰金江，董鹏辉. 基于改进YOLOX-s的风机叶片表面缺陷检测. 自动化与仪表. 2024(11): 69-73+78 .
3.	蒋小发，郭钏，覃超. 基于Faster R-CNN算法的电动观光车避障系统设计. 装备制造技术. 2024(11): 61-67 .
4.	凌英杰. 基于YOLO、SSD与Faster R-CNN的视频监控目标检测算法优化研究. 现代计算机. 2024(21): 49-53+58 .

Other cited types(4)

Get Citation

PDF

XML

Article views (186) PDF downloads (62) Cited by(8)

Turn off MathJax

Article Contents

Abstract

References

Infrared target detection algorithm based on improved Faster R-CNN