Abstract
Edge detection plays an important role in image processing. With the development of deep learning, the accuracy of edge detection has been greatly improved, and people have more and more requirements for edge detection tasks. Most edge detection algorithms are binary edge detection methods, but there are usually multiple categories of edges in an image. In this paper, we present an accurate multi-category edge detection network Richer-CASENet (R-CASENet). In order to make full use of CNN’s powerful feature expression capabilities, we attempt to use more information from feature map for edge feature extraction and classification. Using the ResNet101 network as the backbone, firstly we merge the building blocks in different composite blocks and down-sample to obtain the feature map. Then we fuse the feature maps in different composite blocks to obtain the final fused classifier. Experiments show that we achieved better results on a public dataset.
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