Abstract
With the development of deep convolution neural networks (CNNs), contour detection has made great progress. Some contour detectors based on CNNs have better performance than human beings on standard benchmarks. However, it is easier for CNNs to learn the similar features of adjacent pixels, and the number of background pixels and edge pixels in the input training sample is highly imbalanced. Therefore, the prediction edge by the edge detector based on CNNs is thick and requires post-processing to obtain crisp edges. Accordingly, we introduce a novel parallel attention model and a novel loss function that combines cross-entropy and dice loss through the use of adaptive coefficients, and propose a novel bidirectional multiscale refinement network (BMRN) that stacks multiple refinement modules in order to achieve richer feature representation. The experimental results show that our method has better performance than the state-of-the-art on BSDS500 (ODS F-score of 0.828), NYUDv2 depth datasets (ODS F-score of 0.778) and Multi-Cue dataset (ODS F-score 0.905(0.002)).
Highlights
Contour is an important feature of images, and accurate edge detection is a basic task of machine vision and image processing
With the development of deep learning, convolution neural networks (CNNs) has become the main method of edge detection, such as N4-field[26], Deep Contour[11], Deep Edge[4], HED[12], and richer convolution features (RCF)[13]
In view of the above problems, this paper proposes a bidirectional multiscale refinement network based on BDCN [42]
Summary
Contour is an important feature of images, and accurate edge detection is a basic task of machine vision and image processing. With the development of deep learning, CNN has become the main method of edge detection, such as N4-field[26], Deep Contour[11], Deep Edge[4], HED[12], and RCF[13] These make full use of the hierarchical feature learning ability of neural networks and obtain optimal F-score performance in benchmark datasets such as BSDS500 and NYUDv2. It can be found from the previous literature that deep learning methods pay more attention to the precision and recall of contour, and less attention to the crispness of boundary (precisely localizing edge pixels) than traditional methods. The BMRN model uses a multi-scale training method to train on the enhanced Berkeley segmentation dataset (BSDS500), the NYUDv2 depth dataset, and Multi-Cue in order to further improve its generalization performance
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