结合多尺度及密集特征图融合的阴影检测方法

Shadow Detection Method Combining Multi-Scale and Dense Feature Map Fusion

为了提高图像中阴影检测的准确性,提出一种利用深度神经网络实现阴影检测的方法。首先,构造了一种密集特征图融合结构,将不同卷积层产生的特征图进行融合;其次,针对图像中阴影的多种尺度特征,设计了一种串并联结合的扩张卷积结构提取图像中阴影多尺度特征;最后,将串并联结合的扩张卷积结构和密集特征图融合结构进行结合,设计出一种端到端的Dilated Dense Fusion-Unet网络实现阴影检测功能。实验结果表明,所提方法在SBU和UCF阴影检测数据集上的阴影检测结果及量化评估均优于已有代表性的阴影检测方法,在2个数据集上的准确率分别提高5.8%和6.5%,平衡误差率分别降低2.2%和0.5%。

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.