Advanced Search
中文
Volume 44 Issue 9
Sep.  2022
Turn off MathJax
Article Contents
Abstract
References
Related Articles
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

  • Related Articles

    [1]LI Xianjing, HAO Zhenghui. Infrared Thermal Imaging Smoke Detection Based on Motion and Fuzzy Features[J]. Infrared Technology , 2024, 46(3): 325-331.
    [2]ZHENG Kai, LUO Zhitao, ZHANG Hui. Research Status of Infrared Thermography in NDT of FRP Composites/Thermal Barrier Coatings and Its Development[J]. Infrared Technology , 2023, 45(10): 1008-1019.
    [3]GONG Jiamin, WU Yijie, LIU Fang, ZHANG Yunsheng, LEI Shutao, ZHU Zehao. Image Fusion Algorithm Based on Improved Fuzzy C-means Clustering[J]. Infrared Technology , 2023, 45(8): 849-857.
    [4]JIN Meixiu, ZHU Shihu, WANG Tong, ZHUANG Feifei. Nondestructive Crack Testing via Infrared Thermal Imaging Using Halogen Lamp Excitation[J]. Infrared Technology , 2022, 44(4): 421-427.
    [5]ZHANG Qingyu, FAN Yugang, GAO Yang. Defect Detection of Eddy-Current Thermography Based on Single-Scale Retinex and Improved K-means Clustering[J]. Infrared Technology , 2020, 42(10): 1001-1006.
    [6]KONG Songtao, HUANG Zhen, YANG Jinru. Research Status and Development of Image Processing for Infrared Thermal Image Nondestructive Testing[J]. Infrared Technology , 2019, 41(12): 1133-1140.
    [7]ZHENG Kai, JIANG Haijun, CHEN Li. Infrared Thermography NDT and Its Development[J]. Infrared Technology , 2018, 40(5): 401-411.
    [8]Numerical Simulation of Lock-in Thermograpy for Infrared Nondestructive Testing[J]. Infrared Technology , 2013, (2): 119-122.
    [9]ZHAO Jing-yuan, WANG Li-ming, LIU Bin. The Finite Element Simulation and Analysis of the Infrared NDT for Inner Defects in Casting Product[J]. Infrared Technology , 2008, 30(7): 429-432. DOI: 10.3969/j.issn.1001-8891.2008.07.016
    [10]XIE Xing-sheng, YAN Fang, LU Jia-jia, YE Yu-tang, DENG Jun-jie, WEI Jian-ying, SUN Guo-dong, FANG Liang. The Applications of Thermal Wave NDT in Turbine Blades Testing[J]. Infrared Technology , 2007, 29(9): 552-555. DOI: 10.3969/j.issn.1001-8891.2007.09.015

  • Cited by

    Periodical cited type(3)

    1. 王茜萌. 基于行为聚类的电子商务恶意支付用户检测. 信息与电脑(理论版). 2023(03): 25-27 .
    2. 杜玉红，张松奇. 基于红外图像的耐腐蚀船舶材料表面缺陷识别研究. 舰船科学技术. 2023(14): 152-155 .
    3. 苗勃. 基于红外图像增强算法的石油储罐内油品温度过高风险自动识别方法. 化工自动化及仪表. 2023(06): 900-904 .

    Other cited types(10)

Catalog

    Get Citation
    PDF
    XML
    Article views PDF downloads 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