CHEN Gao, WANG Weihua, LIN Dandan. Infrared Vehicle Target Detection Based on Convolutional Neural Network without Pre-training[J]. Infrared Technology , 2021, 43(4): 342-348.
Citation: CHEN Gao, WANG Weihua, LIN Dandan. Infrared Vehicle Target Detection Based on Convolutional Neural Network without Pre-training[J]. Infrared Technology , 2021, 43(4): 342-348.

Infrared Vehicle Target Detection Based on Convolutional Neural Network without Pre-training

More Information
  • Received Date: July 16, 2020
  • Revised Date: July 29, 2020
  • To tackle the over-dependence of convolutional neural network-based target detection algorithms on pre-training weights, especially for target detection of infrared scenarios under data-sparse conditions, the incorporation of attention modules is proposed to alleviate the degradation of detection performance owing to the absence of pre-training. This paper is based on the YOLO v3 algorithm, which incorporates SE and CBAM modules in a network that mimics human attentional mechanisms to recalibrate the extracted features at the channel and spatial levels. Different weights are adaptively assigned to the features according to their importance, which ultimately improves the detection accuracy. On the constructed infrared vehicle target dataset, the attention module significantly improved the detection accuracy of the non-pre-trained convolutional neural network. Furthermore, the detection accuracy of the network incorporating the CBAM module was 86.3 mAP, demonstrating that the attention module can improve the feature extraction ability of the network and free the network from over-reliance on the pretrained weights.
  • [1]
    Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66. DOI: 10.1109/TSMC.1979.4310076
    [2]
    Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005: 886-893.
    [3]
    Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293-300. DOI: 10.1023/A:1018628609742
    [4]
    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 and Machine Intelligence, 2015, 39(6): 1137-1149 DOI: 10.1109/TPAMI.2016.2577031
    [5]
    QIN Z, LI Z, ZHANG Z, et al. ThunderNet: Towards real-time generic object detection on mobile devices[C]//Proceedings of the IEEE International Conference on Computer Vision, 2019: 6718-6727.
    [6]
    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.
    [7]
    DUAN K, BAI S, XIE L, et al. Centernet: keypoint triplets for object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2019: 6569-6578.
    [8]
    TANG T, ZHOU S, DENG Z, et al. Vehicle detection in aerial images based on region convolutional neural networks and hard negative example mining[J]. Sensors, 2017, 17(2): 336. DOI: 10.3390/s17020336
    [9]
    DENG J, DONG W, Socher R, et al. Imagenet: a large-scale hierarchical image database[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009: 248-255.
    [10]
    LIN T Y, Maire M, Belongie S, et al. Microsoft coco: common objects in context[C]//Proceedings of the European Conference on Computer Vision, 2014: 740-755.
    [11]
    Redmon J, Farhadi A. YOLOv3: An incremental improvement[DB/OL]. https://arxiv.org/abs/1804.02767.2020-0703.
    [12]
    HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
    [13]
    Woo S, Park J, Lee J Y, et al. Cbam: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany: Springer, 2018: 3-19.
    [14]
    HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
    [15]
    Krishna K, Murty M N. Genetic K-means algorithm[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1999, 29(3): 433-439. DOI: 10.1109/3477.764879
  • Cited by

    Periodical cited type(10)

    1. 火久元,苏泓瑞,武泽宇,王婷娟. 基于改进YOLOv8的道路交通小目标车辆检测算法. 计算机工程. 2025(01): 246-257 .
    2. 应保胜,刘畅然,熊豪,石兵华,许小伟. 基于改进GAN的图像去雨方法及其在车辆检测上的应用. 计算机应用与软件. 2025(03): 183-189 .
    3. 沈凌云,郎百和,宋正勋,温智滔. 基于DCS-YOLOv8模型的红外图像目标检测方法. 红外技术. 2024(05): 565-575 . 本站查看
    4. 聂磊,武丽丽,黄一凡,刘梦然,刘江林. 基于红外图像分析的TSV内部缺陷识别方法研究. 仪表技术与传感器. 2023(01): 38-43 .
    5. 曹紫绚,刘刚,张文波,刘森,刘中华. 改进回归损失的深度学习单阶段红外飞机检测. 电光与控制. 2023(04): 28-33 .
    6. 张梦颖,耿蕊. 红外车辆检测中的深度特征处理技术综述. 激光杂志. 2023(09): 11-18 .
    7. 张睿,李允臣,王家宝,李阳,苗壮. 基于深度学习的红外目标检测综述. 计算机技术与发展. 2023(11): 1-8 .
    8. 车启谣,严运兵. 基于改进YOLOv3的行人检测研究. 智能计算机与应用. 2022(08): 8-13 .
    9. 梁秀满,邵彭娟,刘振东,赵恒斌. 自适应特征融合的轻量级交通标志检测方法. 电子测量技术. 2022(23): 107-112 .
    10. 汤双霞. 基于TensorFlow.js和JSDoop的神经网络训练. 信息技术与信息化. 2021(07): 68-69+75 .

    Other cited types(5)

Catalog

    Article views (304) PDF downloads (66) Cited by(15)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return