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| Shadow Detection Method Combining Multi-Scale and Dense Feature Map Fusion |
| ZHANG Shi-hui1,2,ZHANG Xiao-wei1,LI He1,ZHANG Xiao-xiao1,NIU Jing-chun1,CHEN Qi1 |
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, Hebei 066004, China |
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Abstract In order to improve the accuracy of shadow detection in the image, a shadow detection method utilizing deep neural network is proposed. Firstly, a dense feature map fusion structure is proposed to fuse the feature maps generated by different convolutional layers. Secondly, a serial-parallel dilated convolution structure is designed to extract the multi-scale feature in the original image aiming to the scale variant phenomena in shadow detection task. Finally, combining the dense feature map fusion structure and serial-parallel dilated convolution structure, an end-to-end dilated dense fusion-unet is constructed to detect shadow. Experimental results demonstrate that the shadow detection results and quantitative evaluation of the proposed method on the SBU and UCF shadow detection datasets outperform the existing representative shadow detection methods, the accuracy on the two datasets increased by 5.8% and 6.5%, and the balance error rate decreased by 2.2% and 0.5%, respectively. The ablation study verifies the structure rationality of the proposed dilated dense fusion-unet.
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Received: 01 July 2019
Published: 24 May 2021
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| Fund:The National Natural Science Foundation of China |
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［1］Okabe T, Sato I, Sato Y. Attached shadow coding: Estimating surface normals from shadows under unknown reflectance and lighting conditions［C］// International Conference on Computer Vision. Kyoto, Japan, 2009: 1693-1700.
［2］Jean-Franois L, Efros A A, Narasimhan S G. Estimating natural illumination from a single outdoor image［C］// International Conference on Computer Vision. Kyoto, Japan, 2009: 183-190.
［3］毛翠丽, 卢荣胜, 董敬涛, 等. 相移条纹投影三维形貌测量技术综述［J］. 计量学报, 2018, 39(5): 628-640.
Mao C L, Lu R S, Dong J T, et al. Overview of the 3D profilometry of phase shifting fringe projection［J］. Acta Metrologica Sinica, 2018, 39(5): 628-640.
［4］Panagopoulos A, Samaras D, Paragios N. Robust shadow and illumination estimation using a mixture model［C］// Computer Vision and Pattern Recognition. Miami, Florida, United States, 2009: 651-657.
［5］Panagopoulos A, Wang C, Samaras D, et al. Simultaneous cast shadows, illumination and geometry inference using hypergraphs［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(2): 437-449.
［6］范亚兵, 黄桂平, 姚思远, 等. 三目立体摄影测量系统中相机标定技术研究［J］. 计量学报, 2015, 36(2): 132-135.
Fan Y B, Huang G P, Yao S Y, et al. Study on the techniques of camera calibration in the trinocular stereo photogrammetry system［J］. Acta Metrologica Sinica, 2015, 36(2): 132-135.
［7］Junejo I N. Foroosh H. Estimating geo-temporal location of stationary cameras using shadow trajectories［C］// European Conference on Computer Vision. Marseille, France, 2008: 318-331.
［8］Karsch K, Hedau V, Forsyth D. Rendering synthetic objects into legacy photographs［C］// ACM SIGGRAPH Asia. Hong Kong, China, 2011.
［9］Zhu J, Samuel K G G, Masood S Z, et al. Learning to recognize shadows in monochromatic natural images［C］// Computer Vision and Pattern Recognition. San Francisco, California, United States, 2010: 223-230.
［10］Jean-Franois L, Efros A A, Narasimhan S G. Detecting ground shadows in outdoor consumer photographs［C］// European Conference on Computer Vision. Heraklion, Crete, Greece, 2010: 322-335.
［11］Guo R, Dai Q, Hoiem D. Single-image shadow detection and removal using paired regions［C］// Computer Vision and Pattern Recognition, Colorado Springs, Coloradoa, United States, 2011, 2033-2040.
［12］Vicente T F Y, Hoai M, Samaras D. Leave-one-out kernel optimization for shadow detection and removal［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(3): 682-695.
［13］Khan S H, Bennamoun M, Sohel F, et al. Automatic feature learning for robust shadow detection［C］// Computer Vision and Pattern Recognition. Columbus, Ohio, United States, 2014: 1939-1946.
［14］Khan S H, Bennamoun M, Sohel F, et al. Automatic shadow detection and removal from a single image［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2016, 38(3): 431-446.
［15］Vicente T F Y, Hou L, Yu C P. Large-Scale training of shadow detectors with noisily-annotated shadow examples［C］// European Conference on Computer Vision. Amsterdam, Netherlands, 2016: 816-832.
［16］Hosseinzadeh S, Shakeri M, Zhang H. Fast shadow detection from a single image using a patched convolutional neural network ［C］// International Conference on Intelligent Robots and Systems. Madrid, Spain, 2018: 3124-3129.
［17］Nguyen V, Vicente T F Y, Zhao M. Shadow detection with conditional generative adversarial networks［C］// International Conference on Computer Vision. Venice, Italy, 2017: 4520-4528.
［18］Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation［C］// Medical Image Computing and Computer-Assisted Intervention. Munich, Free State of Bavaria, Germany, 2015: 234-241. |
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2021, Vol. 42 
