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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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