Richer Convolutional Features for Edge Detection

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Richer Convolutional Features for Edge Detection
Richer Convolutional Features for Edge Detection
Journal Title:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publication Date:
31 October 2018
Liu, Y., Cheng, M.-M., Hu, X., Bian, J.-W., Zhang, L., Bai, X., & Tang, J. (2019). Richer Convolutional Features for Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(8), 1939–1946. doi:10.1109/tpami.2018.2878849
Edge detection is a fundamental problem in computer vision. Recently, convolutional neural networks (CNNs) have pushed forward this field significantly. Existing methods which adopt specific layers of deep CNNs may fail to capture complex data structures caused by variations of scales and aspect ratios. In this paper, we propose an accurate edge detector using richer convolutional features (RCF). RCF encapsulates all convolutional features into more discriminative representation, which makes good usage of rich feature hierarchies, and is amenable to training via backpropagation. RCF fully exploits multiscale and multilevel information of objects to perform the image-to-image prediction holistically. Using VGG16 network, we achieve state-of-the-art performance on several available datasets. When evaluating on the well-known BSDS500 benchmark, we achieve ODS F-measure of 0.811 while retaining a fast speed (8 FPS). Besides, our fast version of RCF achieves ODS F-measure of 0.806 with 30 FPS. We also demonstrate the versatility of the proposed method by applying RCF edges for classical image segmentation.
License type:
Publisher Copyright
Funding Info:
This research was supported by NSFC (NO. 61620106008, 61572264), the national youth talent support program, Tianjin Natural Science Foundation for Distinguished Young Scholars (NO. 17JCJQJC43700), Huawei Innovation Research Program.
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