Edge detection constitutes a fundamental task in computer vision, serving as a pivotal component for applications such as image segmentation, object detection, and scene understanding. However, existing deep learning-based methods exhibit a critical yet often overlooked limitation: a reliance on pixel-wise loss functions (e.g., Binary Cross Entropy). By treating each pixel independently, these functions fail to supervise the intrinsic geometric properties of edges, such as continuity, smoothness, and directional consistency. This supervision gap is the root cause of common prediction artifacts, including fragmented lines, blurred boundaries, and structurally incoherent outputs. To address this challenge, we propose a novel Edge-Structure-Aware Loss Function as the core contribution of this study. Moving beyond traditional pixel-level matching, this function explicitly incorporates gradient constraints to enforce edge sharpness, continuity constraints to prevent fragmentation, and directional consistency constraints to ensure orientation coherence. Furthermore, we introduce a Three-Stage Dynamic Loss Scheduling Strategy, which leverages a curriculum learning process to guide the network from basic pixel-level learning to refined structural optimization. To provide a robust backbone for this structure-aware supervision, we design a dual-branch network architecture that integrates fine-grained semantic edge features with global contextual information via an efficient fusion module. Extensive experiments demonstrate that our method achieves highly competitive performance across multiple benchmarks, yielding ODS=0.847/OIS=0.861 on BSDS500, ODS=0.899/OIS=0.907 on Multicue, and ODS=0.761/OIS=0.776 on NYUDv2.
Jiang et al. (2026) studied this question.