End-to-end edge detection via improved transformer model.

Abstract

Recently, many efficient edge detection methods based on deep learning have emerged and made remarkable achievements. However, there are two fundamental challenges, i.e., the extraction and fusion of different scale features, as well as the sample imbalance, making the performance of edge detection need to be further promoted. In this paper, we propose an end-to-end edge detection method implemented by improved transformer model to promote edge detection by solving multi-scale fusion and sample imbalance. Specifically, based on the transformer model, we design a multi-scale edge extraction module, which utilizes pooling layer and dilated convolution with different rates and kernels, to realize multi-scale feature extraction and fusion. Moreover, we design an efficient loss function to guide the proposed method to fit the distribution of unbalanced positive and negative samples. Extensive experiments conducted on two benchmark data sets prove that the proposed method significantly outperforms state-of-the-art methods in edge detection.