One-Stage Deep Edge Detection Based on Dense-Scale Feature Fusion and Pixel-Level Imbalance Learning

Edge detection, a basic task in the field of computer vision, is an important preprocessing operation for the recognition and understanding of a visual scene. In conventional models, the edge image generated is ambiguous, and the edge lines are also very thick, which typically necessitates the use of nonmaximum suppression (NMS) and morphological thinning operations to generate clear and thin edge images. In this article, we aim to propose a one-stage neural network model that can generate high-quality edge images without postprocessing. The proposed model adopts a classic encoder–decoder framework in which a pretrained neural model is used as the encoder and a multifeature-fusion mechanism that merges the features of each level with each other functions as a learnable decoder. Further, we propose a new loss function that addresses the pixel-level imbalance in the edge image by suppressing the false positive edge information near the true positive edge and the false negative nonedge. The results of experiments conducted on several benchmark datasets indicate that the proposed method achieves state-of-the-art (SOTA) results without using NMS and morphological thinning operations.