CNN-based methods have improved the performance of edge detection in recent years. While a common issue with the most recent methods is that there is a thickness problem in predicting the edge map, and when objects are small or have dense edges, the predicted edge lines are blurred. We know that multi-pooling reduces the resolution of features, and using a balanced cross-entropy loss function will also make the predicted edges thicker. In this paper, we propose a multi-scale feature hybrid network for edge detection to improve edge resolution. Multiple long and short residual connections are set to establish various information propagation pathways. We construct a feature enhancement unit in the up-sampling path of the network to obtain multi-scale features and fuse higher-resolution features. We demonstrate that residual connections in the network can overcome the class-imbalance training problem in edge samples. Moreover, we introduce a new biased cross-entropy loss function to accomplish the training of our network better, which adjusts the weights according to the ratio of edges and non-edges pixels. Compared to other methods, our network can predict clearer and sharper edges with more details. Evaluate the network on BSDS500 and NYUDv2, our method achieves ODS F-measure of 0.832 on the BSDS500 dataset and 0.768 on the NYUD dataset, best than current state-of-the-art results.

近年来，基于CNN的方法提高了边缘检测的性能。尽管最新方法的一个普遍问题是预测边缘贴图时存在厚度问题，但是当对象较小或边缘密集时，预测的边缘线就会模糊。我们知道多池处理会降低特征的分辨率，并且使用平衡的交叉熵损失函数也会使预测的边缘变粗。在本文中，我们提出了一种用于边缘检测的多尺度特征混合网络，以提高边缘分辨率。设置多个长残差连接和短残差连接以建立各种信息传播路径。我们在网络的上采样路径中构造一个特征增强单元，以获得多尺度特征并融合更高分辨率的特征。我们证明了网络中的剩余连接可以克服边缘样本中的类不平衡训练问题。此外，我们引入了新的有偏交叉熵损失函数来更好地完成我们的网络训练，该函数根据边缘和非边缘像素的比率来调整权重。与其他方法相比，我们的网络可以更详细地预测更清晰，更锐利的边缘。在BSDS500和NYUDv2上评估网络，我们的方法在BSDS500数据集上获得的ODS F测度为0.832，在NYUD数据集上达到了0.768，优于当前的最新结果。与其他方法相比，我们的网络可以更详细地预测更清晰，更锐利的边缘。在BSDS500和NYUDv2上评估网络，我们的方法在BSDS500数据集上获得的ODS F测度为0.832，在NYUD数据集上达到了0.768，优于当前的最新结果。与其他方法相比，我们的网络可以更详细地预测更清晰，更锐利的边缘。在BSDS500和NYUDv2上评估网络，我们的方法在BSDS500数据集上获得的ODS F测度为0.832，在NYUD数据集上达到了0.768，优于当前的最新结果。