• Journal of Geo-information Science
  • Vol. 22, Issue 1, 88 (2020)
Hongchao FAN1、1、*, Wanzhi LI2、2, and Chaoquan ZHANG1、1、2、2
Author Affiliations
  • 1Norwegian University of Science and Technology, Trondheim 7491, Norway
  • 1挪威科技大学,特隆赫姆7491
  • 2Wuhan University, Wuhan 430072, China
  • 2武汉大学,武汉430072
  • show less
    DOI: 10.12082/dqxxkx.2020.190424 Cite this Article
    Hongchao FAN, Wanzhi LI, Chaoquan ZHANG. Anchor-Free Traffic Sign Detection[J]. Journal of Geo-information Science, 2020, 22(1): 88 Copy Citation Text show less
    References

    [1] et alRoad traffic sign detection and classification[J]. IEEE Transactions on Industrial Electronics, 44, 848-859(1997).

    [2] et alTraffic sign detection and recognition using features combination and random forests[J]. International Journal of Advanced Computer Science and Applications, 7, 683-693(2016).

    [3] An active vision system for real-time traffic sign recognition[C]. Intelligent Transportation Systems, 2000. Proceedings, IEEE, 52-57(2000).

    [4] 徐迪红, 唐炉亮. 基于颜色和标志边缘特征的交通标志检测[J]. 武汉大学学报·信息科学版, 2008,33(4):433-436. [ Xu DH, Tang LL. A pyramid-based cracks statistical model fot massive pavement images[J]. Geomatics and Information Science of Wuhan University, 2008,33(4):433-436. ] [ Xu D H, Tang L L. A pyramid-based cracks statistical model fot massive pavement images[J]. Geomatics and Information Science of Wuhan University, 2008,33(4):433-436. ]

    [5] 张静, 何明一, 戴玉超, 等. 多特征融合的圆形交通标志检测[J]. 模式识别与人工智能, 2011,24(2):226-232. [ ZhangJ, He MY, Dai YC, et al. Mutil-feature fusion based circular traffic sigh detection[J]. Patten Recognition and Artifitial Intelligence, 2011,24(2):226-232. ] [ Zhang J, He M Y, Dai Y C, et al. Mutil-feature fusion based circular traffic sigh detection[J]. Patten Recognition and Artifitial Intelligence, 2011,24(2):226-232. ]

    [6] 贾永红, 胡志雄, 周明婷, 等. 自然场景下三角形交通标志的检测与识别[J]. 应用科学学报, 2014,32(4):423-426. [ Jia YH, Hu ZX, Zhou MT, et al. Detection and recognition of triangular traffic signs in natural scenes[J]. Journal of Applied Sciences, 2014,32(4):423-426. ] [ Jia Y H, Hu Z X, Zhou M T, et al. Detection and recognition of triangular traffic signs in natural scenes[J]. Journal of Applied Sciences, 2014,32(4):423-426. ]

    [7] Rapid object detection using a boosted cascade of simple features[J]. Computer Vision and Pattern Recognition, 1, 3(2001).

    [8] et alA robust multi-class traffic sign detection and classification system using asymmetric and symmetric features[C]. IEEE International Conference on Systems, Man and Cybernetics. IEEE Press, 3421-3427(2009).

    [9] Rapid multiclass traffic sign detection in high-resolution images[J]. IEEE Transactions on Intelligent Transportation Systems, 15, 2394-2403(2014).

    [10] Histograms of oriented gradients for human detection[C]. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. IEEE, 886-893(2005).

    [11] et alUnifying visual saliency with HOG feature learning for traffic sign detection[J]. Intelligent Vehicles Symposium IEEE, 24-29(2009).

    [12] et alRich feature hierarchies for accurate object detection and semantic segmentation[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, 580-587(2014).

    [13] et alFaster r-cnn: Towards real-time object detection with region proposal networks[C]. Advances in neural information processing systems, 91-99(2015).

    [14] et alR-fcn: Object detection via region-based fully convolutional networks[C]. Advances in neural information processing systems, 379-387(2016).

    [15] Cascade r-cnn: Delving into high quality object detection[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 6154-6162(2018).

    [16] et alDenseBox: Unifying landmark localization with end to end object detection[J]. Computer Science, 12-19(2015).

    [17] et alSsd: Single shot multibox detector[C]. European conference on computer vision. Springer, Cham, 21-37(2016).

    [18] et alFocal loss for dense object detection[C]. Proceedings of the IEEE international conference on computer vision, 2980-2988(2017).

    [19] et alMicrosoft coco: Common objects in context[C]. European conference on computer vision. Springer, Cham, 740-755(2014).

    [20] et alMetaanchor: Learning to detect objects with customized anchors[C]. Advances in Neural Information Processing Systems., 320-330(2018).

    [21] Cornernet: Detecting objects as paired keypoints[C]. Proceedings of the European Conference on Computer Vision (ECCV), 734-750(2018).

    [22] Bottom-up object detection by grouping extreme and center points[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition., 850-859(2019).

    [23] et alDensebox: Unifying landmark localization with end to end object detection[J]. arXiv preprint arXiv:1509.04874(2015).

    [24] et alUnitbox: An advanced object detection network[C]. Proceedings of the 24th ACM international conference on Multimedia. ACM, 516-520(2016).

    [25] et alFCOS: Fully Convolutional One-Stage Object Detection[J]. arXiv preprint arXiv:1904.01355(2019).

    [26] et alFoveaBox: Beyond anchor-based object detector[J]. arXiv preprint arXiv:1904.03797(2019).

    [27] et alYou only look once: Unified, real-time object detection[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, 779-788(2016).

    [28] [J]. Very deep convolutional networks for large-scale image recognition[J]. Computer Science, 2014, 34-46.

    [29] et alDeformable convolutional networks[C]. Proceedings of the IEEE international conference on computer vision., 764-773(2017).

    [30] et alDeformable convnets v2: More deformable, better results[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition., 9308-9316(2019).

    [31] et alFeature pyramid networks for object detection[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition., 2117-2125(2017).

    [32] Squeeze-and-excitation networks[C]. Proceedings of the IEEE conference on computer vision and pattern recognition., 7132-7141(2018).

    [33] [J]. http://benchmark.ini.rub.de/?section=gtsdb&subsection=news

    [34] et alMask r-cnn[C]. Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2980-2988(2017).

    [35] Fast r-cnn[C]. Proceedings of the IEEE international conference on computer vision., 1440-1448(2015).

    [36] et alDssd: Deconvolutional single shot detector[J]. arXiv preprint arXiv:1701.06659(2017).

    [37] et alGoing deeper with convolutions[C]. Proceedings of the IEEE conference on computer vision and pattern recognition., 1-9(2015).

    [38] Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]. International Conference on International Conference on Machine Learning, 423-434(2015).

    [39] et alRethinking the inception architecture for computer vision[C]. Proceedings of the IEEE conference on computer vision and pattern recognition., 2818-2826(2016).

    [40] et alInception-v4, inception-resnet and the impact of residual connections on learning[C]. Thirty-First AAAI Conference on Artificial Intelligence(2017).

    [41] et alShufflenet v2: Practical guidelines for efficient cnn architecture design[C]. Proceedings of the European Conference on Computer Vision (ECCV), 116-131(2018).

    Hongchao FAN, Wanzhi LI, Chaoquan ZHANG. Anchor-Free Traffic Sign Detection[J]. Journal of Geo-information Science, 2020, 22(1): 88
    Download Citation