Correct and Crisp Edge Detection Approach Based on Dense Network

Edge detection is a basic problem in computer vision and image processing. The main purpose of edge detection is to identify points with obvious brightness changes in digital images. At present, there are many good detection methods, but most of them do not consider the correctness and crispness of edges at the same time. In order to address this problem, this paper proposes a method based on a deep convolution neural network. The method is mainly based on a dense network that is then combined with the single network structure of a backward refinement path module. The former can detect and retain the feature information between different layers in an image. The latter makes full use of the extracted information so that the low-level detail features and high-level abstract features can be better integrated in the final output. We tested the method on the BIPED data set. The results show that the correctness and crispness of the edges can be balanced in the detection process, and the ODS, OIS and AP of this method reach 0.888, 0.893 and 0.916, respectively. Compared with the state-of-the-art approaches, the proposed method improves the standard evaluation by 3%-5%, and the convergence speed is also significantly improved.