基于卷积神经网络的跨层融合边缘检测算法

Cross fusion edge detection algorithm based on CNN

针对目前边缘检测算法因过于依赖全连接层,使得边缘线条粗糙,且损失函数设定不当,造成梯度消失和大量主要特征信息丢失等问题,提出了基于卷积神经网络的交叉融合边缘检测算法。该算法利用1×1多卷积核的梯度方式来降维,完成横纵向图像低级与高级特征对象的采集;然后通过自上而下和自左向右循环卷积流向方式,保证每层的损失函数可以较平稳地前向和反向传播;最后利用跨层交叉融合对图像边缘特征进行细化。实验结果表明,该算法在伯克利大学数据集(BSDS500)上最优数据集规模(ODS)F-measure为0.806,接近人类平均视觉感知。

Aiming at the problems that the current edge detection algorithms rely on the FC layer too much,making the edge line rough and the improper setting of the loss function,which make the gradient disappear and the main characteristics loss.To solve the two problems,this paper proposed a cross-layer fusion edge detection algorithm based on convolutional neural network.It used 1×1 multiple convolution kernels gradient method to reduce the dimension,and completed the acquisition of low-level and high-level feature objects of horizontal and vertical images.Then,the convolutional flowing direction was from top to bottom and from left to right,so that the loss function of each layer could be propagated forward and backward smoothly.Finally,it refined the image edge feature by cross-layer fusion.Experimental results show that ODS F-measure is 0.806 on BSDS500 dataset,which is close to human average visual perception.