Computer Applications and Software, Volume. 42, Issue 4, 257(2025)

FEATURE LEVEL FUSION DETECTION ALGORITHM OF VISIBLE AND INFRARED IMAGES BASED ON IMPROVED YOLOv5

Liang Siyuan1, Dou Fei2, Xie Shating2, Zhao Hongyi1, and Tian Qing1
Author Affiliations
  • 1School of Information, North China University of Technology, Beijing 100144, China
  • 2Beijing Mass Transit Railway Operation Corp.LTD., Beijing 100044, China
  • show less
    References(15)

    [3] [3] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.

    [4] [4] Girshick R. Fast R-CNN[C]//IEEE International Conference on Computer Vision, 2015: 1440-1448.

    [5] [5] Ren S Q, He K M, 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.

    [6] [6] Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector[C]//European Conference on Computer Vision, 2016: 21-37.

    [7] [7] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.

    [8] [8] Redmon J, Farhadi A. Yolo9000: Better, faster, stronger[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6517-6525.

    [9] [9] Redmon J, Farhadi A. YOLOv3: An incremental improvement[EB]. arXiv: 1804.02767, 2018.

    [10] [10] Wang C Y, Wang C Y, Liao H Y. YOLOv4: Optimal speed and accuracy of object detection[EB]. arXiv: 2004.10934v1, 2020.

    [13] [13] Li H, Wu X J. DenseFuse: A fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 2019, 28(5): 2614-2623.

    [14] [14] Wu Y, Chen Y P, Yuan L, et al. Rethinking classification and localization for object detection[EB]. arXiv: 1904.06493, 2020.

    [15] [15] Wang C Y, Liao H Y, Wu Y H, et al. CSPNET: A new backbone that can enhance learning capability of CNN[C]//IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2019.

    [16] [16] Zheng Z H, Wang P, Liu W, et al. Distance-IoU loss: Faster and better learning for bounding box regression[EB]. arXiv: 1911.08287, 2019.

    [17] [17] Huang G, Liu Z, Maaten L, et al. Densely connected convolutional networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2261-2269.

    [18] [18] Prabhakar K R, Srikar V S, Babu R V. DeepFuse: A deep unsupervised approach for exposure fusion with extreme exposure image pairs[C]//IEEE International Conference on Computer Vision, 2017: 4724-4732.

    [19] [19] Ge Z, Liu S T, Wang F, et al. YOLOX: Exceeding YOLO series in 2021[EB]. arXiv: 2107.08430, 2021.

    Tools

    Get Citation

    Copy Citation Text

    Liang Siyuan, Dou Fei, Xie Shating, Zhao Hongyi, Tian Qing. FEATURE LEVEL FUSION DETECTION ALGORITHM OF VISIBLE AND INFRARED IMAGES BASED ON IMPROVED YOLOv5[J]. Computer Applications and Software, 2025, 42(4): 257

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Feb. 10, 2022

    Accepted: Aug. 25, 2025

    Published Online: Aug. 25, 2025

    The Author Email:

    DOI:10.3969/j.issn.1000-386x.2025.04.037

    Topics