Refined edge detection model based on RCF

Abstract

Edge detection is a fundamental method in image processing and computer vision. Aiming to address the issues of roughness and blurriness in edges generated by deep learning-based edge detection technology, a refined edge detection(RED) model based on richer convolutional features(RCF) for edge detection was proposed. In this model, RCF was used as the baseline network. Some downsampling operations in the backbone network were removed, and the coordinate attention(CA) module and hybrid dilated convolution were added to the backbone network. The number and parameters of the compression layers were changed in the deep supervision module, and smooth compression for reducing feature dimensionality was adopted. In the final fusion module, a cross-layer cross-fusion module was used to fuse the information from high and low layers. The RED model was trained and tested on the extended BSDS500 dataset. The optimal dataset scale(ODS) and the optimal image scale(OIS) of the dataset were 0.809 and 0.832, respectively, as evaluated on the BSDS500 benchmark. The experimental results showed that RED model extracted clearer and more detailed edge contours, and the extracted edge information was more comprehensive and abundant.

Keywords

deep learning / edge detection / dilated convolution / coordinate attention / cross-layer fusion

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