现有的基于卷积神经网络的边缘检测算法,通常可以给出图像中每个像素为边缘的概率,即边缘概率图。针对边缘概率图细化后边缘存在丢失、间断等问题,提出一种基于梯度掩模滤波的边缘细化算法。为了获得高梯度掩模和低梯度掩模,引入基于Canny边缘检测算法的双阈值方法。对于高梯度掩模滤波后的边缘概率图进行增强,并对低梯度掩膜滤波后的边缘概率图进行削弱。最后,对边缘概率图进行非极大值抑制,得到二值的边缘图。实验结果表明,所提的边缘细化算法具有更多的连续边缘,并且更符合单边缘响应准则。

Current edge detection algorithms based on convolutional neural network usually give the probability that each pixel in the image is an edge, namely the edge probability map. To address the problems of edge loss and discontinuity after edge probability map thinning, an edge thinning algorithm based on a gradient mask filter is proposed. To obtain the high-gradient and low-gradient masks, a dual threshold method based on the Canny edge detection algorithm is introduced. Then, an edge probability map filtered using the high gradient mask is enhanced, and that filtered by the low gradient mask is weakened. Finally, we performed non-maximum suppression on an edge probability map to obtain a binary edge map. The experimental results indicate that the proposed edge thinning algorithm provides more continuous edges and conforms to the single-edge response criterion.