LOU Zhehang, LUO Suyun. Vehicle Infrared Target Detection Based on YOLOX and Swin Transformer[J]. Infrared Technology , 2022, 44(11): 1167-1175.
Citation: LOU Zhehang, LUO Suyun. Vehicle Infrared Target Detection Based on YOLOX and Swin Transformer[J]. Infrared Technology , 2022, 44(11): 1167-1175.

Vehicle Infrared Target Detection Based on YOLOX and Swin Transformer

More Information
  • Received Date: June 09, 2022
  • Revised Date: August 09, 2022
  • Owing to the problems of high noise and poor contrast in infrared images, the accuracy of target detection is easily reduced. Here, an improved YOLOX model combined with YOLOX and a Swin Transformer is proposed. To improve the feature extraction ability, reduce the activation functions and standardization layers of the neck and head parts in YOLOX, and optimize the network structure, the Swin Transformer is used to replace the CSPDarknet backbone extraction network in YOLOX. This study tests the improved model on both the InfiRay and FILR datasets. The obtained experimental results indicate that the improved YOLOX network has significantly improved the average detection accuracy on both datasets and is more suitable for infrared image target detection.
  • [1]
    Caniou J. Passive Infrared Detection: Theory and Applications[M]. Springer Science & Business Media, 2013.
    [2]
    任章, 李露, 蒋宏. 基于红外图像序列的运动目标检测算法研究[J]. 红外与激光工程, 2007, 36(9): 136-140. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ2007S2032.htm

    REN Zhang, LI Lu, JIANG Hong Research on moving target detection algorithm based on infrared image sequence[J]. Infrared and Laser Engineering, 2007, 36(9): 136-140. https://www.cnki.com.cn/Article/CJFDTOTAL-HWYJ2007S2032.htm
    [3]
    吴燕茹, 程咏梅, 赵永强. 利用KPCA特征提取的Adaboost红外标检测[J]. 红外与激光工程, 2011, 40(2): 338-343. DOI: 10.3969/j.issn.1007-2276.2011.02.032

    WU Yanru, CHENG Yongmei, ZHAO Yongqiang. Adaboost infrared target detection using KPCA feature extraction[J]. Infrared and Laser Engineering, 2011, 40(2): 338-343. DOI: 10.3969/j.issn.1007-2276.2011.02.032
    [4]
    陈炳文. 特定视场中红外成像目标检测关键技术研究[D]. 武汉: 武汉大学, 2013.

    CHEN Bingwen. Research on Key Technologies of Infrared Imaging Target Detection in Specific Field of View[D] Wuhan: Wuhan University, 2013.
    [5]
    James W Davis, Vinay Sharma. Robust background-subtraction for person detection in thermal imagery[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004: 1-8.
    [6]
    Ei Baf Fida, Bouwmans Thierry, Vachon Bertrand. Fuzzy foreground detection for infrared video[C]//IEEE Computer society Conference on Computer Vision and Pattern Recognition, 2008: 1-6.
    [7]
    于杰. 基于红外摄像机的夜间场景监控方法研究与实现[D]. 北京: 北京邮电大学, 2013.

    YU Jie. Research and Implementation of Night Scene Monitoring Method Based on Infrared Camera[D]. Beijing: Beijing University of Posts and Telecommunications, 2013.
    [8]
    易诗, 聂焱, 张洋溢, 等. 基于红外热成像与YOLOv3的夜间目标识别方法[J]. 红外技术, 2019, 41(10): 970-975. http://hwjs.nvir.cn/article/id/hwjs201910013

    YI Shi, NIE Yan, ZHANG Yangyi, et al. Night target recognition method based on infrared thermal imaging and YOLOv3[J]. Infrared Technology, 2019, 41(10): 970-975. http://hwjs.nvir.cn/article/id/hwjs201910013
    [9]
    聂霆. 基于红外图像的前方车辆识别与车距检测[D]. 西安: 西安电子科技大学, 2015.

    NIE Ting. Forward Vehicle Recognition and Distance Detection Based on Infrared Image[D]. Xi'an: Xi'an University of Electronic Science and Technology, 2015.
    [10]
    陈谧. 基于深度学习的红外目标检测方法研究与实现[D]. 成都: 电子科技大学, 2021.

    CHEN Mi. Research and Implementation of Infrared Target Detection Method Based on Depth Learning[D]. Chengdu: University of Electronic Science and Technology, 2021.
    [11]
    舒朗, 张智杰, 雷波. 一种针对红外目标检测的Dense-Yolov5算法研究[J]. 光学与光电技术, 2021, 19(1): 69-75. https://www.cnki.com.cn/Article/CJFDTOTAL-GXGD202101010.htm

    SHU Lang, ZHANG Zhijie, LEI Bo. Research on Dense-Yolov5 algorithm for infrared target detection[J]. Optics and Optoelectronics, 2021, 19(1): 69-75. https://www.cnki.com.cn/Article/CJFDTOTAL-GXGD202101010.htm
    [12]
    LIU Z, LIN Y T, CAO Y, et al. Swin transformer: hierarchical vision transformer using shifted windows[J/OL]. arXiv Preprint arXiv, 2103.14030.
    [13]
    GE Z, LIU S, WANG F, et al. Yolox: Exceeding yolo seriesin[J/OL]. arXiv Preprint arXiv, 2107.08430.
    [14]
    Redmon J, Farhadi A. YOLO V3: an incremental improvement[J/OL]. arXiv Preprint arXiv, 1804.02767.
    [15]
    Bochkovskiy A, WANG C Y, LIAO H M. YOLOv4: optimal speed and accuracy of object detection[J/OL]. arXiv Preprint arXiv, 2004.10934.
    [16]
    ZHUANG L, Hanzi M, CHAO Yuan W, et al. A ConvNet for the 2020s[J/OL]. arXiv Preprint arXiv, 2201.03545.
    [17]
    王周春, 崔文楠, 张涛. 基于支持向量机的长波红外目标分类识别算法[J]. 红外技术, 2021, 43(2): 153-161. http://hwjs.nvir.cn/article/id/73b78f5d-26f5-4da8-8b5c-93aa7a7a40e2

    WANG Zhouchun, CUI Wennan, ZHANG Tao. Long wave infrared target classification and recognition algorithm based on support vector machine [J]. Infrared Technology, 2021, 43(2): 153-161. http://hwjs.nvir.cn/article/id/73b78f5d-26f5-4da8-8b5c-93aa7a7a40e2
    [18]
    Inf iray. Double light vehicle scene database[EB/OL]. [2022-04-02]. http://iray.iraytek.com:7813/apply/Double_light_vehicle.html/.
    [19]
    Flir. FLIR Thermal Data Set[EB/OL]. [2022-04-02]. https://www.flir.com/oem/adas/adas-dataset-form/.
    [20]
    张汝榛, 张建林, 祁小平, 等. 复杂场景下的红外目标检测[J]. 光电工程, 2020, 47(10): 128-137. https://www.cnki.com.cn/Article/CJFDTOTAL-GDGC202010010.htm

    ZHANG Ruzhen, ZHANG Jianlin, QI Xiaoping, et al. Infrared target detection in complex scenes[J]. Optoelectronic Engineering, 2020, 47(10): 128-137. https://www.cnki.com.cn/Article/CJFDTOTAL-GDGC202010010.htm
    [21]
    张鹏辉, 刘志, 郑建勇, 等. 面向嵌入式系统的复杂场景红外目标实时检测算法[J]. 光子学报, 2022, 51(2): 203-212. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202202021.htm

    ZHANG Penghui, LIU Zhi, ZHENG Jianyong, et al. Real time infrared target detection algorithm for embedded systems in complex scenes[J]. Acta Photonica Sinica, 2022, 51(2): 203-212. https://www.cnki.com.cn/Article/CJFDTOTAL-GZXB202202021.htm
    [22]
    宋甜, 李颖, 王静. 改进YOLOv5s的车载红外图像目标检测[J]. 现代计算机, 2022, 28(2): 21-28. https://www.cnki.com.cn/Article/CJFDTOTAL-XDJS202202003.htm

    SONG Tian, LI Ying, WANG Jing. Improved vehicle infrared image target detection of YOLOv5s[J]. Modern Computer, 2022, 28(2): 21-28. https://www.cnki.com.cn/Article/CJFDTOTAL-XDJS202202003.htm
  • Cited by

    Periodical cited type(6)

    1. 林斌,刘亚军,吴燕东,董晋国. 煤矿带式输送机自换电巡检机器人关键技术研究. 煤炭技术. 2025(03): 248-250 .
    2. 常凯旋,黄建华,孙希延,罗键,包世涛,黄焕生. 基于双模态图像融合的无人机光学小目标检测算法. 激光与光电子学进展. 2025(04): 279-293 .
    3. 宋冬梅. 基于模糊数学理论的灰度图像边缘信息智能检测方法. 电子设计工程. 2025(08): 130-135 .
    4. 杨家全,李邦源,丁贞煜,马文龙,汪航,孙宏滨. 基于多重先验的无监督学习红外图像增强算法. 云南电力技术. 2024(02): 33-40 .
    5. 贺养慧. 基于生成对抗网络的可见光和红外图像融合研究. 激光杂志. 2024(10): 120-124 .
    6. 周君,高焱,姜晴. 双边滤波下的低光照激光雷达图像超分辨增强技术. 激光杂志. 2024(12): 131-137 .

    Other cited types(2)

Catalog

    Article views PDF downloads Cited by(8)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return