Advanced Search
中文
Volume 44 Issue 9
Sep.  2022
Turn off MathJax
Article Contents
Abstract
References
KUANG Chuwen, HE Wang. Object Detection Algorithm Based on Infrared and Visible Light Images[J]. Infrared Technology , 2022, 44(9): 912-919.
Citation: KUANG Chuwen, HE Wang. Object Detection Algorithm Based on Infrared and Visible Light Images[J]. Infrared Technology , 2022, 44(9): 912-919.
shu

Object Detection Algorithm Based on Infrared and Visible Light Images

  • 1.

    Huizhou Economics and Polytechnic College, Huizhou 516057, China

  • 2.

    School of Computer Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China

More Information
  • Received Date: November 28, 2021
  • Revised Date: January 27, 2022
    Graphical Abstract
    • Abstract
  • A target detection method based on infrared and visible image fusion is proposed to overcome the shortcomings of the existing target detection algorithms based on visible light. In this method, depth separable convolution and the residual structure are combined to construct a parallel high-efficiency feature extraction network to extract the object information of infrared and visible images, respectively. Simultaneously, the adaptive feature fusion module is introduced to fuse the features of the corresponding scales of the two branches through autonomous learning such that the two types of image information are complementary. Finally, the deep and shallow features are fused layer by layer using the feature pyramid structure to improve the detection accuracy of different scale targets. Experimental results show that the proposed network can completely integrate the effective information in infrared and optical images and realize target recognition and location on the premise of ensuring accuracy and efficiency. Moreover, in the actual substation equipment detection scene, the network shows good robustness and generalization ability and can efficiently complete the detection task.
    • FullText(HTML)
    • References (19)
  • [1]
    孙怡峰, 吴疆, 黄严严, 等. 一种视频监控中基于航迹的运动小目标检测算法[J]. 电子与信息学报, 2019, 41(11): 2744-2751. https://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201911028.htm

    SUN Yifeng, WU Jiang, HUANG Yan, et al. A track based moving small target detection algorithm in video surveillance [J]. Journal of Electronics and Information, 2019, 41(11): 2744-2751. https://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201911028.htm
    [2]
    LIN C, LU J, GANG W, et al. Graininess-aware deep feature learning for pedestrian detection[J]. IEEE Transactions on Image Processing, 2020, 29: 3820-3834. DOI: 10.1109/TIP.2020.2966371
    [3]
    范丽丽, 赵宏伟, 赵浩宇, 等. 基于深度卷积神经网络的目标检测研究综述[J]. 光学精密工程, 2020, 28(5): 161-173. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM202005019.htm

    FAN Lili, ZHAO Hongwei, ZHAO Haoyu, et al. Overview of target detection based on deep convolution neural network[J]. Optical Precision Engineering, 2020, 28(5): 161-173. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM202005019.htm
    [4]
    赵永强, 饶元, 董世鹏, 等. 深度学习目标检测方法综述[J]. 中国图象图形学报, 2020, 288(4): 5-30. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB202004001.htm

    ZHAO Yongqiang, RAO yuan, DONG Shipeng, et al. Overview of deep learning target detection methods[J]. Chinese Journal of Image and Graphics, 2020, 288(4): 5-30. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB202004001.htm
    [5]
    罗会兰, 彭珊, 陈鸿坤. 目标检测难点问题最新研究进展综述[J]. 计算机工程与应用, 2021, 57(5): 36-46. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202105005.htm

    LUO Huilan, PENG Shan, CHEN Hongkun. Overview of the latest research progress on difficult problems of target detection[J]. Computer Engineering and Application, 2021, 57(5): 36-46. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202105005.htm
    [6]
    郝永平, 曹昭睿, 白帆, 等. 基于兴趣区域掩码卷积神经网络的红外-可见光图像融合与目标识别算法研究[J]. 光子学报, 2021, 50(2): 15-16. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202102010.htm

    HAO Yongping, CAO Zhaorui, BAI fan, et al. Research on infrared visible image fusion and target recognition algorithm based on region of interest mask convolution neural network[J]. Acta Photonica Sinica, 2021, 50(2): 15-16. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202102010.htm
    [7]
    李舒涵, 许宏科, 武治宇. 基于红外与可见光图像融合的交通标志检测[J]. 现代电子技术, 2020, 43(3): 45-49. https://www.cnki.com.cn/Article/CJFDTOTAL-XDDJ202003012.htm

    LI Shuhan, XU Hongke, WU Zhiyu. Traffic sign detection based on infrared and visible image fusion [J]. Modern Electronic Technology, 2020, 43(3): 45-49. https://www.cnki.com.cn/Article/CJFDTOTAL-XDDJ202003012.htm
    [8]
    XIAO X, WANG B, MIAO L, et al. Infrared and visible image object detection via focused feature enhancement and cascaded semantic extension[J]. Remote Sensing, 2021, 13(13): 2538.
    [9]
    Banuls A, Mandow A, Vazquez-Martin R, et al. Object detection from thermal infrared and visible light cameras in search and rescue scenes[C]// 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). IEEE, 2020: 380-386.
    [10]
    李章维, 胡安顺, 王晓飞. 基于视觉的目标检测方法综述[J]. 计算机工程与应用, 2020, 56(8): 7-15. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202008002.htm

    LI Zhangwei, HU Anshun, WANG Xiaofei. Overview of vision based target detection methods[J]. Computer Engineering and Application, 2020, 56(8): 7-15. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202008002.htm
    [11]
    汪廷. 红外图像与可见光图像融合研究与应用[D]. 西安: 西安理工大学, 2019.

    WANG Ting. Research and Application of Infrared Image and Visible Image Fusion[D]. Xi'an: Xi'an University of Technology, 2019.
    [12]
    XIANG X, LV N, YU Z, et al. Cross-modality person re-identification based on dual-path multi-branch network[J]. IEEE Sensors Journal, 2019, 19(23): 11706-11713.
    [13]
    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 & Machine Intelligence, 2017, 39(6): 1137-1149.
    [14]
    Bochkovskiy A, WANG C Y, LIAO H. YOLOv4: Optimal speed and accuracy of object detection[J/OL]. Arxiv Preprint Arxiv, https://arxiv.org/abs/2004.10934.
    [15]
    LIU W, Anguelov D, Erhan D, et al. SSD: Single Shot MultiBox Detector[C]// European Conference on Computer Vision, 2016: 21-37.
    [16]
    TIAN Z, SHEN C, CHEN H, et al. FCOS: Fully convolutional one-stage object detection[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2020: 9626-9635.
    [17]
    DUAN K, XIE L, QI H, et al. Location-sensitive visual recognition with cross-IOU loss[J/OL]. Arxiv Preprint Arxiv, https://arxiv.org/abs/2104.04899.
    [18]
    LI C, ZHAO N, LU Y, et al. Weighted sparse representation regularized graph learning for RGB-T object tracking[C]// ACM on Multimedia Conference. ACM, 2017: 1856-1864.
    [19]
    白玉, 侯志强, 刘晓义, 等. 基于可见光图像和红外图像决策级融合的目标检测算法[J]. 空军工程大学学报, 2020, 21(6): 53-59. https://www.cnki.com.cn/Article/CJFDTOTAL-KJGC202006009.htm

    BAI Yu, HOU Zhiqiang, LIU Xiaoyi, et al. Target detection algorithm based on decision level fusion of visible and infrared images[J]. Journal of Air Force Engineering University, 2020, 21(6): 53-59. https://www.cnki.com.cn/Article/CJFDTOTAL-KJGC202006009.htm
    • Related Articles

  • Cited by

    Periodical cited type(7)

    1. 邓长征，刘明泽，付添，弓萌庆，骆冰洁. 基于改进YOLOv7-Tiny的变电设备红外图像识别. 红外技术. 2025(01): 44-51 . 本站查看
    2. 万军，周凯，何文磊. 基于分组特征提取的轻量型多源目标检测. 红外技术. 2025(03): 307-315 . 本站查看
    3. 秦伟，段俊阳. 基于特征相似性的红外与可见光图像融合方法. 激光杂志. 2024(02): 119-123 .
    4. 关晓丹，郑东平，肖成. 基于全卷积网络的复杂背景红外弱小目标检测研究. 激光杂志. 2024(04): 254-258 .
    5. 吴建华，白永福，谢永生，秦会会. 多源信息融合的巡检机器人目标检测算法研究. 无线电工程. 2023(08): 1925-1933 .
    6. 王文霞，张文，何凯. 基于双模态特征增强的目标检测算法研究与应用. 激光与红外. 2023(09): 1364-1374 .
    7. 陈茹，雷旭，杨琦. 激光超声技术下的运动损伤非接触检测方法. 内蒙古师范大学学报(自然科学汉文版). 2023(06): 638-644 .

    Other cited types(6)

Catalog

    Get Citation
    PDF
    XML
    Article views (801) PDF downloads (179) Cited by(13)
    Related

    Copyright © 《Infrared Technology》Editorial Office滇ICP备12000354号-3

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return