LI Xiangrong, SUN Lihui. Multiscale Infrared Target Detection Based on Attention Mechanism[J]. Infrared Technology , 2023, 45(7): 746-754.
Citation: LI Xiangrong, SUN Lihui. Multiscale Infrared Target Detection Based on Attention Mechanism[J]. Infrared Technology , 2023, 45(7): 746-754.

Multiscale Infrared Target Detection Based on Attention Mechanism

More Information
  • Received Date: April 09, 2022
  • Revised Date: July 19, 2022
  • To address the problems of poor textural detail, low contrast, and poor target detection in infrared images, a multiscale infrared target detection model that integrates a channel attention mechanism is proposed based on Yolov4 (You Only Look Once version 4). First, the number of model parameters is reduced by reducing the depth of the backbone feature extraction network. Second, to supplement the shallow high-resolution feature information, the multiscale feature fusion module is reconstructed to improve the utilization of the feature information. Finally, before the multiscale feature map is generated, the channel attention mechanism is integrated to further improve the infrared feature extraction ability and reduce noise interference. The experimental results show that the size of the algorithm model in this study was only 28.87% of the Yolov4. The detection accuracy of the infrared targets also significantly improved.
  • [1]
    史泽林, 冯斌, 冯萍. 基于波前编码的无热化红外成像技术综述(特邀)[J]. 红外与激光工程, 2022, 51(1): 32-42. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202201003.htm

    SHI Zelin, FENG Bin, FENG Ping. An overview of non thermal infrared imaging technology based on wavefront coding (invited) [J]. Infrared and Laser Engineering, 2022, 51(1): 32-42. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ202201003.htm
    [2]
    CHEN C, LI H, WEI Y, et al. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience & Remote Sensing, 2013, 52(1): 574-581.
    [3]
    LIU R, LU Y, GONG C, et al. Infrared point target detection with improved template matching[J]. Infrared Physics & Technology, 2012, 55(4): 380-387.
    [4]
    Teutsch M, Muller T, Huber M, et al. Low resolution person detection with a moving thermal infrared camera by hot spot classification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Columbus, 2014: 209­216.
    [5]
    HAO Q, ZHANG L, WU X, et al. Multiscale object detection in infrared streetscape images based on deep learning and instance level data augmentation[J]. Applied Sciences, 2019, 9(3): 565. DOI: 10.3390/app9030565
    [6]
    Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
    [7]
    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. https://www.cnki.com.cn/Article/CJFDTOTAL-BJLG202307012.htm
    [8]
    REN S, HE K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(6): 1137-1149.
    [9]
    刘智嘉, 汪璇, 赵金博, 等. 基于YOLO算法的红外图像目标检测的改进方法[J]. 激光与红外, 2020, 50(12): 1512-1520. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW202012015.htm

    LIU Zhijia, WANG Xuan, ZHAO Jinbo, et al. An improved method of infrared image target detection based on YOLO algorithm[J]. Laser and Infrared, 2020, 50(12): 1512-1520. https://www.cnki.com.cn/Article/CJFDTOTAL-JGHW202012015.htm
    [10]
    Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv: 1804.02767, 2018.
    [11]
    HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Pro-ceedings of the IEEE Conference On Computer Vision and Pattern Recognition, 2018: 7132-7141.
    [12]
    Bochkovskiy A, Wang C Y, LIAO H Y M. Yolov4: Optimal speed and accuracy of object detection[J/OL]. arXiv preprint arXiv: 2004.10934, 2020.
    [13]
    LIN T Y, Dollar P, Girshick R, et al. Feature pyramid networks for object detection[C]//Computer Vision and Pattern Recognition(CVPR), 2017: 2117-2125.
    [14]
    LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]//Computer Vision and Pattern Recognition(CVPR), 2018: 8759-8768.
    [15]
    LUO Y, CAO X, ZHANG J, et al. CE-FPN: enhancing channel information for object detection[J/OL]. arXiv preprint arXiv: 2103. 10643, 2021.
    [16]
    谢俊章, 彭辉, 唐健峰, 等. 改进YOLOv4的密集遥感目标检测[J]. 计算机工程与应用, 2021, 57(22): 247-256. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202122029.htm

    XIE Junzhang, PENG Hui, TANG Jianfeng, et al. Improved dense remote sensing target detection of YOLOv4[J]. Computer Engineering and Application, 2021, 57(22): 247-256. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202122029.htm
    [17]
    鞠默然, 罗江宁, 王仲博, 等. 融合注意力机制的多尺度目标检测算法[J]. 光学学报, 2020, 40(13): 132-140. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB202013016.htm

    JU Muran, LUO Jiangning, WANG Zhongbo, et al. Multi scale target detection algorithm integrating attention mechanism[J]. Journal of Optics, 2020, 40(13): 132-140. https://www.cnki.com.cn/Article/CJFDTOTAL-GXXB202013016.htm
    [18]
    TAN M, PANG R, LE Q V. Efficient det: Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 10781-10790.
    [19]
    LIU W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector[C]//European Conference on Computer Vision, 2016: 21-37.
    [20]
    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, 2016: 779-788.
    [21]
    Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 7263-727.
  • Cited by

    Periodical cited type(6)

    1. 吕鹏远,兰金江,曾学仁,牛霈,方亮,赵松璞. 基于特征增强与融合的红外目标检测算法. 红外技术. 2024(07): 782-790 . 本站查看
    2. 陈永麟,王恒涛,张上. 基于YOLO v7的轻量级红外目标检测算法. 红外技术. 2024(12): 1380-1389 . 本站查看
    3. 张玉彬,刘鹏谦,陈丽娜,韩雅鸽,刘蕊,谢静,徐长航. 基于YOLO v5的带涂层钢结构亚表面缺陷脉冲涡流热成像智能检测. 红外技术. 2023(10): 1029-1037 . 本站查看
    4. 张睿,李允臣,王家宝,李阳,苗壮. 基于深度学习的红外目标检测综述. 计算机技术与发展. 2023(11): 1-8 .
    5. 郭柏璋,牟琦,冀汶莉. 融合注意力机制的YOLOv5深度神经网络杂草识别方法. 无线电工程. 2023(12): 2771-2782 .
    6. 范晓畅,梁煜,张为. 基于改进Shuffle-RetinaNet的红外车辆检测算法. 激光与光电子学进展. 2023(24): 118-127 .

    Other cited types(2)

Catalog

    Article views (185) PDF downloads (43) Cited by(8)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return