路面裂缝检测是道路运营和维护的一项重要工作,由于裂缝没有固定形状而且纹理特征受光照影响大,基于图像的精确裂缝检测是一项巨大的挑战。本文针对裂缝图像的特点,提出了一种U型结构的卷积神经网络UCrackNet。首先在跳跃连接中加入Dropout层来提高网络的泛化能力;其次,针对上采样中容易产生边缘轮廓失真的问题,采用池化索引对图像边界特征进行高保真恢复;最后,为了更好地提取局部细节和全局上下文信息,采用不同扩张系数的空洞卷积密集连接来实现感受野的均衡,同时嵌入多层输出融合来进一步提升模型的检测精度。在公开的道路裂缝数据集CrackTree206和AIMCrack上测试表明,该算法能有效地检测出路面裂缝,并且具有一定的鲁棒性。
基于U型全卷积神经网络的路面裂缝检测
作者单位信息

出版日期:2020年12月22日
摘要
参考文献
[1] http://www.zgjtb.com/2019-10/08/content_230254.htm.
[2] Schnebele E, Tanyu B F, Cervone G, et al. Review of remote sensing methodologies for pavement management and assessment[J]. European Transport Research Review, 2015, 7(2): 7.
[3] Zhang D J, Li Q Q. A review of pavement high speed detection technology[J]. Journal of Geomatics, 2015, 40(1): 1–8.
张德津, 李清泉. 公路路面快速检测技术发展综述[J]. 测绘地理信息, 2015, 40(1): 1–8.
[4] Shi Y, Cui L M, Qi Z Q, et al. Automatic road crack detection using random structured forests[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(12): 3434–3445.
[5] Xu W, Tang Z M, Lv J Y. Pavement crack detection based on image saliency[J]. Journal of Image and Graphics, 2013, 18(1): 69–77.
徐威, 唐振民, 吕建勇. 基于图像显著性的路面裂缝检测[J]. 中国图象图形学报, 2013, 18(1): 69–77.
[6] Zhang L, Yang F, Zhang Y D, et al. Road crack detection using deep convolutional neural network[C]//International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 2016: 3708–3712.
[7] Cha Y J, Choi W, Büyük?ztürk O. Deep learning-based crack damage detection using convolutional neural networks[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361–378.
[8] Maeda H, Sekimoto Y, Seto T, et al. Road damage detection and classification using deep neural networks with smartphone images[J]. Computer–Aided Civil and Infrastructure Engineering, 2018, 33(12): 1127–1141.
[9] Carr T A, Jenkins M D, Iglesias M I, et al. Road crack detection using a single stage detector based deep neural network[C]//2018 IEEE Workshop on Environmental, Energy and Structural Monitoring Systems, Salerno, Italy, 2018.
[10] Bang S, Park S, Kim H, et al. Encoder–decoder network for pixel-level road crack detection in black-box images[J]. Computer-Aided Civil and Infrastructure Engineering, 2019, 34(8): 713–727.
[11] Yang X C, Li H, Yu Y T, et al. Automatic pixel-level crack detection and measurement using fully convolutional network[J]. Computer–Aided Civil and Infrastructure Engineering, 2018, 33(12): 1090–1109.
[12] Chen L C, Zhu Y K, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//European Conference on Computer Vision, Glasgow, United Kingdom, 2018: 833–851.
[13] Yang F, Zhang L, Yu S J, et al. Feature pyramid and hierarchical boosting network for pavement crack detection[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(4): 1525–1535.
[14] Zou Q, Zhang Z, Li Q Q, et al. DeepCrack: learning hierarchical convolutional features for crack detection[J]. IEEE Transactions on Image Processing, 2019, 28(3): 1498–1512.
[15] Mei Q P, Gül M, Azim M R, et al. Densely connected deep neural network considering connectivity of pixels for automatic crack detection[J]. Automation in Construction, 2020, 110: 103018.
[16] Fei Y, Wang K C P, Zhang A, et al. Pixel-level cracking detection on 3D asphalt pavement images through deep-learning-based CrackNet-V[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(1): 273–284.
[17] Zhang A, Wang K C P, Li B X, et al. Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network[J]. Computer–Aided Civil and Infrastructure Engineering, 2017, 32(10): 805–819.
[18] Ronneberger O, Fischer P, Brox T, et al. U-Net: Convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru, 2015: 234–241.
[19] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C]//International Conference on Learning Representations, 2015.
[20] Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481–2495.
[21] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[C]//International Conference on Learning Representations, 2016.
[22] Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834–848.
[23] Wang P Q, Chen P F, Yuan Y, et al. Understanding convolution for semantic segmentation[C]//2018 IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, USA, 2018: 1451–1460.
[24] Yang M K, Yu K, Zhang C, et al. DenseASPP for semantic segmentation in street scenes[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018: 3684–3692.
[25] Luo W J, Li Y J, Urtasun R, et al. Understanding the effective receptive field in deep convolutional neural networks[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems, 2016: 4905–4913.
[26] Xie S, Tu Z. Holistically-Nested Edge Detection[J]. International Journal of Computer Vision, 2015, 125(1-3): 3–18.
[27] Zou Q, Cao Y, Li Q Q, et al. CrackTree: Automatic crack detection from pavement images[J]. Pattern Recognition Letters, 2012, 33(3): 227–238.
[28] Kingma D P, Ba L J. Adam: A method for stochastic optimization[C]//International Conference on Learning Representations, Ithaca, NY, 2015.
[29] Chaurasia A, Culurciello E. LinkNet: Exploiting encoder representations for efficient semantic segmentation[C]//2007 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA, 2017: 1–4.
[30] Zhang Z X, Liu Q J, Wang Y H. Road extraction by deep residual U-Net[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5): 749–753.
[31] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016: 770–778.
基金项目:
国家自然科学基金资助项目(61871350);浙江省自然科学基金资助(GG19E050005)
导出参考文献,格式为:
引用本文:
陈涵深, 姚明海, 瞿心昱. 基于U型全卷积神经网络的路面裂缝检测[J]. 光电工程, 2020, 47(12): 200036.
下一篇:防潮密封型光纤连接器