Advanced Search
中文
Volume 45 Issue 5
May  2023
Turn off MathJax
Article Contents
Abstract
References
Related Articles
ZHENG Lu, PENG Yueping, ZHOU Tongtong. A Lightweight Infrared Target Detection Algorithm for Multi-scale Targets[J]. Infrared Technology , 2023, 45(5): 474-481.
Citation: ZHENG Lu, PENG Yueping, ZHOU Tongtong. A Lightweight Infrared Target Detection Algorithm for Multi-scale Targets[J]. Infrared Technology , 2023, 45(5): 474-481.
shu

A Lightweight Infrared Target Detection Algorithm for Multi-scale Targets

  • 1.

    Postgraduate Brigade, Engineering University of PAP, Xi'an 710086, China

  • 2.

    College of Information Engineering, Engineering University of PAP, Xi'an 710086, China

More Information
  • Received Date: June 04, 2022
  • Revised Date: June 22, 2022
    Graphical Abstract
    • Abstract
  • To solve the problems of large parameters, high complexity, and poor detection performance of multiscale targets in the existing infrared target detection algorithms based on deep learning, a lightweight infrared target detection algorithm for multiscale targets is proposed. Based on YOLOv3, the algorithm uses the MobileNet V2 backbone network, simplified spatial pyramid structure (simSPP), anchor-free mechanism, decoupling head, and simplified positive and negative sample allocation strategies (SimOTA) to optimize the backbone, neck, and head, respectively. Finally, LMD-YOLOv3 with the model size of 6.25 M and floating-point computation of 2.14 GFLOPs was obtained. Based on the MTS-UAV data set, the mAP reached 90.5%, and on the RTX2080Ti dataset, the FPS reached 99. Compared with YOLOv3, mAP increased by 11.7%, and the model size was only 1/10 of YOLOv3.
    • FullText(HTML)
    • References (17)
  • [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]
    Girshick R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440-1448.
    [3]
    Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. Advances in neural Information Processing Systems, 2015, 28: 91-99.
    [4]
    LIU W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector[C]//European conference on computer vision., 2016: 21-37.
    [5]
    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
    [6]
    WANG C Y, Yeh I H, LIAO H Y M. You Only Learn One Representation: Unified Network for Multiple Tasks[J/OL]. arXiv pre-print arXiv, 2021, https://arxiv.org/abs/2105.04206.
    [7]
    GE Z, LIU S, WANG F, et al. Yolox: Exceeding yolo series in 2021[J/OL]. arXiv preprint arXiv, 2021, https://arxiv.org/abs/2107.08430.
    [8]
    LIU M, DU H, ZHAO Y, et al. Image small target detection based on deep learning with SNR controlled sample generation[M]//Current Trends in Computer Science and Mechanical Automation, 2018: 211-220.
    [9]
    LIN Liangkui, WANG Shaoyou, TANG Zhongxing. Using deep learning to detect small targets in infrared oversampling images[J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 947-952. DOI: 10.21629/JSEE.2018.05.07
    [10]
    ZHAO D, ZHOU H, RANG S, et al. An adaptation of CNN for small target detection in the infrared[C]//IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, 2018: 669-672.
    [11]
    谢江荣. 基于深度学习的空中红外目标检测关键技术研究[D]. 上海: 中国科学院大学(中国科学院上海技术物理研究所), 2019.

    XIE Jiangrong. Research on Key Technologies of Air Infrared Target Detection Based on Deep Learning[D] Shanghai: University of Chinese Academy of Sciences (Shanghai Institute of Technical Physics, Chinese Academy of Sciences), 2019.
    [12]
    FAN M, TIAN S, LIU K, et al. Infrared small target detection based on region proposal and CNN classifier[J]. Signal, Image and Video Processing, 2021, 15: 1927-1936. DOI: 10.1007/s11760-021-01936-z
    [13]
    张凯, 刘昊, 杨曦, 等. 基于关键点检测网络的空中红外目标要害部位识别算法[J]. 西北工业大学学报, 2020, 38(6): 1154-1162. DOI: 10.3969/j.issn.1000-2758.2020.06.003

    ZHANG K, LIU H, YANG X, et al. Key position recognition algorithm of aerial infrared target based on key point detection net-work [J]. Journal of Northwest University of Technology, 2020, 38(6): 1154-1162 DOI: 10.3969/j.issn.1000-2758.2020.06.003
    [14]
    Redmon J, Farhadi A. Yolov3: An incremental improvement[J/OL]. arXiv preprint arXiv, 2018, https://arxiv.org/abs/1804.02767.
    [15]
    Howard A, Zhmoginov A, CHEN L C, et al. Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation[J/OL]. Computer Science, 2018, https://arxiv.org/abs/1801.04381v2.
    [16]
    Howard A G, ZHU M, CHEN B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[J/OL]. arXiv preprint arXiv, 2017, https://arxiv.org/abs/1704.04861.
    [17]
    HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 37(9): 1904-1916. DOI: 10.1109/TPAMI.2015.2389824
    • Related Articles

  • Related Articles

    [1]CHEN Xiaohan, XU Yuanyuan. Infrared Multi-Scale Target Detection Algorithm Based on RCR-YOLO[J]. Infrared Technology , 2025, 47(4): 459-467.
    [2]LIU Xin, ZHANG Bin. Electronic Zooming of Infrared Image Based on Lightweight Multi-scale Aggregation Network[J]. Infrared Technology , 2025, 47(4): 445-452.
    [3]YE Baicheng, ZHU Youpan, ZHOU Yongkang, DUAN Chenhao, ZHANG Yudong, TAO Zhigang, FU Zhiyu. Review of Lightweight Target Detection Algorithms[J]. Infrared Technology , 2025, 47(3): 289-298.
    [4]CHEN Yonglin, WANG Hengtao, ZHANG Shang. Lightweight Infrared Target Detection Algorithm Based on YOLO v7[J]. Infrared Technology , 2024, 46(12): 1380-1389.
    [5]SHAO Yanhua, HUANG Qimeng, MEI Yanying, ZHANG Xiaoqiang, CHU Hongyu, WU Yadong. Multi-scale Anchor Construction Method for Object Detection[J]. Infrared Technology , 2024, 46(2): 162-167.
    [6]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.
    [7]CHEN Yanlin, WANG Zhishe, SHAO Wenyu, YANG Fan, SUN Jing. Multi-scale Transformer Fusion Method for Infrared and Visible Images[J]. Infrared Technology , 2023, 45(3): 266-275.
    [8]SUN Shixin, ZHENG Zhiyun. Genetic Algorithm for Infrared Multi-target Detection Based on Multi-scale NNLoG Feature[J]. Infrared Technology , 2019, 41(9): 837-842.
    [9]SHEN Xu, CHENG Xiaohui, WANG Xinzheng. Infrared Dim-small Object Detection Algorithm Based on Adaptive Scale Local Contrast Enhancement Combined with Visual Attention Mechanism[J]. Infrared Technology , 2019, 41(8): 764-771.
    [10]WANG Yu-xiang, HAN Zhen-duo, WANG Hong-min. Detection Algorithm for Dim Infrared Target Based on Multi-Difference Factor[J]. Infrared Technology , 2012, 34(6): 351-355. DOI: 10.3969/j.issn.1001-8891.2012.06.009

  • Cited by

    Periodical cited type(1)

    1. 朱文轩. 多尺度目标检测算法在复杂场景下的性能评估与改进. 信息与电脑. 2025(06): 5-7 .

    Other cited types(1)

Catalog

    Get Citation
    PDF
    XML
    Article views (172) PDF downloads (44) Cited by(2)
    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