Advanced Search
中文
Volume 45 Issue 5
May  2023
Turn off MathJax
Article Contents
Abstract
References
ZHOU Jinjie, JI Li, ZHANG Qian, ZHANG Baohui, YUAN Xilin, LIU Yanqing, YUE Jiang. Multiscale Infrared Object Detection Network Based on YOLO-MIR Algorithm[J]. Infrared Technology , 2023, 45(5): 506-512.
Citation: ZHOU Jinjie, JI Li, ZHANG Qian, ZHANG Baohui, YUAN Xilin, LIU Yanqing, YUE Jiang. Multiscale Infrared Object Detection Network Based on YOLO-MIR Algorithm[J]. Infrared Technology , 2023, 45(5): 506-512.
shu

Multiscale Infrared Object Detection Network Based on YOLO-MIR Algorithm

  • 1.

    Nanjing Research Center, Kunming Institute of Physics, kunming 650221, China

  • 2.

    College of Science, Hohai University, Nanjing 210024, China

More Information
  • Received Date: February 05, 2023
  • Revised Date: March 30, 2023
    Graphical Abstract
    • Abstract
  • To address the low detection accuracy and poor robustness of infrared images compared with visible images, a multiscale object detection network YOLO-MIR(YOLO for multiscale IR images) for infrared images is proposed. First, to increase the adaptability of the network to infrared images, the feature extraction and fusion modules were improved to retain more details in the infrared images. Second, the detection ability of multiscale objects is enhanced, the scale of the fusion network is increased, and the fusion of infrared image features is facilitated. Finally, a data augmentation algorithm for infrared images was designed to increase the network robustness. Ablation experiments were conducted to evaluate the impact of different methods on the network performance, and the results show that the network performance was significantly improved using the infrared dataset. Compared with the prevalent algorithm YOLOv7, the average detection accuracy of this algorithm was improved by 3%, the adaptive ability to infrared images was improved, and the accurate detection of targets at various scales was realized.
    • FullText(HTML)
    • References (21)
  • [1]
    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.
    [2]
    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.
    [3]
    LI Z, ZHOU F. FSSD: feature fusion single shot multibox detector[J/OL]. arXiv preprint arXiv, 2017, https://arxiv.org/abs/1712.00960.
    [4]
    Redmon J, Farhadi A. Yolov3: An incremental improvement[J/OL]. arXiv preprint arXiv, 2018, https://arxiv.org/abs/1804.02767.
    [5]
    Jocher G, Chaurasia A, Stoken A, et al. ultralytics/yolov5: v6.1 - TensorRT, TensorFlow Edge TPU and OpenVINO Export and Inference[Z/OL]. 2022, https://doi.org/10.5281/ZENODO.6222936.
    [6]
    Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J/OL]. arXiv preprint arXiv, 2020, https://arxiv.org/abs/2004.10934#:~:text=%EE%80%80YOLOv4%3A%20Optimal%20Speed%20and%20Accuracy%20of%20Object%20Detection%EE%80%81.,features%20operate%20on%20certain%20models%20exclusively%20and%20.
    [7]
    WANG C Y, Bochkovskiy A, LIAO H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[J]. arXiv preprint arXiv, 2022, https://arxiv.org/abs/2207.02696.
    [8]
    LIU S, QI L, QIN H, et al. Path aggregation network for instance segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2018: 8759-8768.
    [9]
    Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger[C]// Conference on Computer Vision & Pattern Recognition. IEEE, 2017: 6517-6525.
    [10]
    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. http://pubmed.ncbi.nlm.nih.gov/27295650/
    [11]
    He K, Gkioxari G, Dollár P, et al. Mask r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2961-2969.
    [12]
    ZHENG Z, WANG P, REN D, et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J]. IEEE Transactions on Cybernetics, 2021, 52(8): 8574-8586. http://www.xueshufan.com/publication/3194790201
    [13]
    Veit A, Matera T, Neumann L, et al. Coco-text: Dataset and benchmark for text detection and recognition in natural images[J]. arXiv preprint arXiv, 2016, https://arxiv.org/abs/1601.07140.
    [14]
    Smith A R. Color gamut transform pairs[J]. ACM Siggraph Computer Graphics, 1978, 12(3): 12-19. DOI: 10.1145/965139.807361
    [15]
    Zhou Z, Cao J, Wang H, et al. Image denoising algorithm via doubly bilateral filtering[C]// International Conference on Information Engineering and Computer Science. IEEE, 2009: 1-4.
    [16]
    Hoiem D, Divvala S K, Hays J H. Pascal VOC 2008 challenge[J]. Computer Science, 2009 https://www.semanticscholar.org/paper/Pascal-VOC-2008-Challenge-Hoiem-Divvala/9c327cf1bb8435a8fba27b6ace50bb907078d8d1.
    [17]
    ZHAO W Y. Discriminant component analysis for face recognition[C]//Proceedings 15th International Conference on Pattern Recognition, IEEE, 2000, 2: 818-821.
    [18]
    Venkataraman V, FAN G, FAN X. Target tracking with online feature selection in FLIR imagery[C]// IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2007: 1-8.
    [19]
    CHEN R, LIU S, MU J, et al. Borrow from source models: efficient infrared object detection with limited examples[J]. Applied Sciences, 2022, 12(4): 1896. DOI: 10.3390/app12041896
    [20]
    Kera S B, Tadepalli A, Ranjani J J. A paced multi-stage block-wise approach for object detection in thermal images[J]. The Visual Computer, 2022, https://doi.org/10.1007/s00371-022-02445-x.
    [21]
    Vadidar M, Kariminezhad A, Mayr C, et al. Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object Detection[C]// IEEE Intelligent Vehicles Symposium (Ⅳ). IEEE, 2022: 367-374.
    • Related Articles

Catalog

    Get Citation
    PDF
    XML
    Article views (263) PDF downloads (59) Cited by()
    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