Volume 16 Issue 5
Sep.  2023
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LIU Yan-lei, LI Meng-zhe, WANG Xuan-xuan. Lightweight YOLOv5s vehicle infrared image target detection[J]. Chinese Optics, 2023, 16(5): 1045-1055. doi: 10.37188/CO.2022-0254
Citation: LIU Yan-lei, LI Meng-zhe, WANG Xuan-xuan. Lightweight YOLOv5s vehicle infrared image target detection[J]. Chinese Optics, 2023, 16(5): 1045-1055. doi: 10.37188/CO.2022-0254

Lightweight YOLOv5s vehicle infrared image target detection

doi: 10.37188/CO.2022-0254
Funds:  Supported by National Natural Science Foundation of China (No. 61905068)
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  • Corresponding author: liuyanlei@htu.edu.cn
  • Received Date: 14 Dec 2022
  • Rev Recd Date: 06 Jan 2023
  • Accepted Date: 24 Mar 2023
  • Available Online: 13 Apr 2023
  • Vehicle infrared image target detection is an important way of road environment perception for autonomous driving. However, existing vehicle infrared image target detection algorithms have defects, such as low memory utilization, complex calculation and low detection accuracy. In order to solve the above problems, an improved YOLOv5s lightweight target detection algorithm is proposed. Firstly, the C3Ghost and Ghost modules are introduced into the YOLOv5s detection network to reduce network complexity. Secondly, the αIoU loss function is introduced to improve the positioning accuracy of the target and the networks training efficiency. Then, the subsampling rate of the network structure is reduced and the KMeans clustering algorithm is used to optimize the prior anchor size to improve the ability to detect of small targets. Finally, coordinate attention and spatial depth convolution modules are respectively introduced into the Backbone and Neck to further optimize the model and improve the feature extraction of the model. The experimental results show that compared with the original YOLOv5s algorithm, the improved algorithm can compress the model size by 78.1%, reduce the number of parameters and Giga Floating-point Operations Per Second by 84.5% and 40.5% respectively, and improve the mean average precision and detection speed by 4.2% and 10.9%, respectively.

     

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  • [1]
    MUHAMMAD K, ULLAH A, LLORET J, et al. Deep learning for safe autonomous driving: current challenges and future directions[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(7): 4316-4336. doi: 10.1109/TITS.2020.3032227
    [2]
    TAKUMI K, WATANABE K, HA Q SH, et al. . Multispectral object detection for autonomous vehicles[C]. Proceedings of the on Thematic Workshops of ACM Multimedia 2017, ACM, 2017: 35-43.
    [3]
    CHOI Y, KIM N, HWANG S, et al. KAIST multi-spectral day/night data set for autonomous and assisted driving[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(3): 934-948. doi: 10.1109/TITS.2018.2791533
    [4]
    LIU Q, ZHUANG J J, MA J. Robust and fast pedestrian detection method for far-infrared automotive driving assistance systems[J]. Infrared Physics &Technology, 2013, 60: 288-299.
    [5]
    任凤雷, 周海波, 杨璐, 等. 基于双注意力机制的车道线检测[J]. 中国光学(中英文),2023,16(3):645-653.

    REN F L, ZHOU H B, YANG L, et al. Lane detection based on dual attention mechanism[J]. Chinese Optics, 2023, 16(3): 645-653. (in Chinese)
    [6]
    WANG H, CAI Y F, CHEN X B, et al. Night-time vehicle sensing in far infrared image with deep learning[J]. Journal of Sensors, 2016, 2016: 3403451.
    [7]
    GALARZA-BRAVO M A, FLORES-CALERO M J. Pedestrian detection at night based on faster R-CNN and far infrared images[C]. Proceedings of the 11th International Conference on Intelligent Robotics and Applications, Springer, 2018: 335-345.
    [8]
    CHEN Y F, XIE H, SHIN H. Multi‐layer fusion techniques using a CNN for multispectral pedestrian detection[J]. IET Computer Vision, 2018, 12(8): 1179-1187. doi: 10.1049/iet-cvi.2018.5315
    [9]
    王驰, 于明坤, 杨辰烨, 等. 抛撒地雷的夜视智能探测方法研究[J]. 中国光学,2021,14(5):1202-1211. doi: 10.37188/CO.2020-0214

    WANG CH, YU M K, YANG CH Y, et al. Night vision intelligent detection method of scatterable landmines[J]. Chinese Optics, 2021, 14(5): 1202-1211. (in Chinese) doi: 10.37188/CO.2020-0214
    [10]
    GONG J, ZHAO J H, LI F, et al. . Vehicle detection in thermal images with an improved yolov3-tiny[C]. Proceedings of 2020 IEEE International Conference on Power, Intelligent Computing and Systems, IEEE, 2020: 253-256.
    [11]
    SUN M Y, ZHANG H CH, HUANG Z L, et al. Road infrared target detection with I‐YOLO[J]. IET Image Processing, 2022, 16(1): 92-101. doi: 10.1049/ipr2.12331
    [12]
    吴海滨, 魏喜盈, 刘美红, 等. 结合空洞卷积和迁移学习改进YOLOv4的X光安检危险品检测[J]. 中国光学,2021,14(6):1417-1425. doi: 10.37188/CO.2021-0078

    WU H B, WEI X Y, LIU M H, et al. Improved YOLOv4 for dangerous goods detection in X-ray inspection combined with atrous convolution and transfer learning[J]. Chinese Optics, 2021, 14(6): 1417-1425. (in Chinese) doi: 10.37188/CO.2021-0078
    [13]
    张印辉, 庄宏, 何自芬, 等. 氨气泄漏混洗自注意力轻量化红外检测[J]. 中国光学(中英文),2023,16(3):607-619.

    ZHANG Y H, ZHUANG H, HE Z F, et al. Lightweight infrared detection of ammonia leakage using shuffle and self-attention[J]. Chinese Optics, 2023, 16(3): 607-619. (in Chinese)
    [14]
    JIANG X H, CAI W, YANG ZH Y, et al. IEPet: a lightweight multiscale infrared environmental perception network[J]. Journal of Physics:Conference Series, 2021, 2078: 012063. doi: 10.1088/1742-6596/2078/1/012063
    [15]
    WU ZH L, WANG X, CHEN CH. Research on lightweight infrared pedestrian detection model algorithm for embedded Platform[J]. Security and Communication Networks, 2021, 2021: 1549772.
    [16]
    XIN X L, PAN F, WANG J CH, et al. . SwinT-YOLOv5s: improved YOLOv5s for vehicle-mounted infrared target detection[C]. Proceedings of the 41st Chinese Control Conference (CCC), IEEE, 2022: 7326-7331.
    [17]
    ZHAI SH P, SHANG D R, WANG SH H, et al. DF-SSD: an improved SSD object detection algorithm based on DenseNet and feature fusion[J]. IEEE Access, 2020, 8: 24344-24357. doi: 10.1109/ACCESS.2020.2971026
    [18]
    DAI X R, YUAN X, WEI X Y. TIRNet: object detection in thermal infrared images for autonomous driving[J]. Applied Intelligence, 2021, 51(3): 1244-1261. doi: 10.1007/s10489-020-01882-2
    [19]
    2022. FREE FLIR Thermal Dataset for Algorithm Training. [Online]. Available: https://www.flir.com/oem/adas/adas-dataset-form.
    [20]
    CAO M L, FU H, ZHU J Y, et al. Lightweight tea bud recognition network integrating GhostNet and YOLOv5[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 12897-12914. doi: 10.3934/mbe.2022602
    [21]
    HE J B, ERFANI S M, MA X J, et al.. Alpha-IoU: a family of power intersection over union losses for bounding box regression[C]. Proceedings of the 34th Advances in Neural Information Processing Systems, 2021.
    [22]
    ZHA M F, QIAN W B, YI W L, et al. A lightweight YOLOv4-Based forestry pest detection method using coordinate attention and feature fusion[J]. Entropy, 2021, 23(12): 1587. doi: 10.3390/e23121587
    [23]
    SUNKARA R, LUO T. No more strided convolutions or pooling: a new CNN building block for low-resolution images and small objects[C]. Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, 2022: 443-459.
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