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面向边缘智能光学感知的航空紧固件旋转检测

符长虹 陈锟辉 鲁昆瀚 郑光泽 赵吉林

符长虹, 陈锟辉, 鲁昆瀚, 郑光泽, 赵吉林. 面向边缘智能光学感知的航空紧固件旋转检测[J]. 应用光学, 2022, 43(3): 472-480. doi: 10.5768/JAO202243.0303002
引用本文: 符长虹, 陈锟辉, 鲁昆瀚, 郑光泽, 赵吉林. 面向边缘智能光学感知的航空紧固件旋转检测[J]. 应用光学, 2022, 43(3): 472-480. doi: 10.5768/JAO202243.0303002
FU Changhong, CHEN Kunhui, LU Kunhan, ZHENG Guangze, ZHAO Jilin. Aviation fastener rotation detection for intelligent optical perception with edge computing[J]. Journal of Applied Optics, 2022, 43(3): 472-480. doi: 10.5768/JAO202243.0303002
Citation: FU Changhong, CHEN Kunhui, LU Kunhan, ZHENG Guangze, ZHAO Jilin. Aviation fastener rotation detection for intelligent optical perception with edge computing[J]. Journal of Applied Optics, 2022, 43(3): 472-480. doi: 10.5768/JAO202243.0303002

面向边缘智能光学感知的航空紧固件旋转检测

doi: 10.5768/JAO202243.0303002
基金项目: 上海市“科技创新行动计划”自然科学基金项目(20ZR1460100)
详细信息
    作者简介:

    符长虹(1986—),男,副教授,工学博士,主要从事智能视觉感知、边缘计算AI视觉系统研制、无人系统自主导航方面的研究。E-mail:changhongfu@tongji.edu.cn

  • 中图分类号: TN925

Aviation fastener rotation detection for intelligent optical perception with edge computing

  • 摘要: 针对航空紧固件分拣过程中现有方法存在效率低、成本高、精度差等问题,提出一种面向边缘智能光学感知的旋转目标检测方法。构建一种基于强化语义和优化空间的特征融合机制,有效提升目标检测模型的性能;设计一种空洞幻影模块,减少特征融合网络的参数量,有利于模型在工业场景下的边缘部署;采用高斯类环形平滑标签方法,在模型检测层预测分支上实现旋转目标检测,显著提升模型检测性能,并更有助于工业机器人自动抓取。在权威公开旋转数据集上,检测准确率达到77.16%。最后,在嵌入式智能设备上进行边缘部署并测试,整体准确率达到99.76%,推理速度超过20 FPS (frames per second),满足工业应用的要求。
  • 图  1  轻量级航空紧固件旋转检测方法

    Fig.  1  Lightweight aviation fastener rotation detection method

    图  2  强化语义与优化空间特征融合机制

    Fig.  2  Enhanced semantics and optimized space feature fusion mechanism

    图  3  空洞幻影模块

    Fig.  3  Dilated ghost module

    图  4  瓶颈层

    Fig.  4  Bottleneck layer

    图  5  旋转框角度定义[19]

    Fig.  5  Angle definition of rotation bounding box[19]

    图  6  典型航空紧固件

    Fig.  6  Typical aviation fasteners

    图  7  OLAFDet检测结果示例

    Fig.  7  Result examples of OLAFDet detection

    表  1  与前沿旋转框检测方法的对比

    Table  1  Comparison with cutting-edge rotation bounding box detection methods

    方法来源参数量/
    MB
    PL/%BD/%BR/%GTF/%SV/%LV/%SH/%TC/%BC/%ST/%SBF/%RA/%HA/%SP/%HC/%mAP/%
    FR-O[20] CVPR2018 242 79.42 77.13 17.70 64.05 35.30 38.02 37.16 89.41 69.64 59.28 50.30 52.91 47.89 47.40 46.30 54.13
    TOSO[21] ICASSP2020 212 80.17 65.59 39.82 39.95 49.71 65.01 53.58 81.45 44.66 78.51 48.85 56.73 64.40 64.24 36.75 57.92
    PIoU
    Loss[22]
    ECCV2020 80.90 69.70 24.10 60.20 38.30 64.40 64.80 90.90 77.20 70.40 46.50 37.10 57.10 61.90 64.00 60.50
    Axis
    Learning[23]
    RS2020 79.53 77.15 38.59 61.15 67.53 70.49 76.30 89.66 79.07 83.53 47.27 61.01 56.28 66.06 36.05 65.98
    MARNet[24] IJRS2021 88.91 77.91 39.88 71.17 62.79 58.96 66.25 90.87 73.73 79.04 57.57 64.33 62.47 61.64 51.80 67.15
    GSDet[25] TIP2021 81.12 76.78 40.78 75.89 64.50 58.37 74.21 89.92 79.40 78.83 64.54 63.67 66.04 58.01 52.13 68.28
    RADet[26] RS2020 79.45 76.99 48.05 65.83 65.46 74.40 68.86 89.70 78.14 74.97 49.92 64.63 66.14 71.58 62.16 69.09
    RoI
    Transformer[27]
    CVPR2019 273 88.64 78.52 43.44 75.92 68.81 73.68 83.59 90.74 77.27 81.46 58.39 53.54 62.83 58.93 47.67 69.56
    BBAVectors[28] WACV2021 276 88.35 79.96 50.69 62.18 78.43 78.98 87.94 90.85 83.58 84.35 54.13 60.24 65.22 64.28 55.70 72.32
    SCRDet[29] ICCV2019 427 89.98 80.65 52.09 68.36 68.36 60.32 72.41 90.85 87.94 86.86 65.02 66.68 66.25 68.24 65.21 72.61
    GLS-Net[30] RS2020 88.65 77.40 51.20 71.03 73.30 72.16 84.68 90.87 80.43 85.38 58.33 62.27 67.58 70.69 60.42 72.96
    R3Det[6] AAAI2021 787 88.76 83.09 50.91 67.27 76.23 80.39 86.72 90.78 84.68 83.24 61.98 61.35 66.91 70.63 53.94 73.79
    FEDet[31] ICCSE2021 89.09 79.87 51.13 70.20 78.42 80.54 87.84 90.86 83.91 85.31 58.33 66.90 67.74 69.74 63.43 74.89
    TricubeNet[32] WACV2022 88.75 82.12 49.24 72.98 77.64 74.53 84.65 90.81 86.02 85.38 58.69 63.59 73.82 69.67 71.08 75.26
    Beyond
    Bounding-Box[33]
    CVPR2021 89.08 83.20 54.37 66.87 81.23 80.96 87.17 90.21 84.32 86.09 52.34 69.94 75.52 80.76 67.96 76.67
    OLAFDet 18 89.28 85.09 48.75 64.65 80.81 84.70 88.09 90.70 86.72 87.41 60.14 67.12 75.02 81.55 67.39 77.16
    下载: 导出CSV

    表  2  消融分析实验结果

    Table  2  Experimental results of ablation analyses

    基准模型OLAFDet
    检测框类型 水平框 旋转框 旋转框 旋转框
    高斯类环形平滑标签
    强化语义和优化空间
    空洞幻影模块
    mAP/% 42.32 76.86 77.17 77.16
    参数量/MB 14.8 15.8 21.3 18.0
    下载: 导出CSV

    表  3  航空紧固件检测的测试结果

    Table  3  Test results of aviation fastener detection %

    类别螺栓垫片螺帽圆柱销铆钉螺钉
    APs100.00100.00100.00100.00100.00100.00
    APm100.00100.0098.9399.9999.12100.00
    AP100.00100.0099.1799.9999.43100.00
    mAP99.76
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-07
  • 修回日期:  2022-04-22
  • 网络出版日期:  2022-04-24
  • 刊出日期:  2022-05-12

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