🤖 AI Summary
Existing weighted binary cross-entropy (WBCE) loss treats all non-edge pixels uniformly, ignoring their structural heterogeneity—leading to blurred edge predictions. To address this, we propose the Edge-Boundary-Texture (EBT) loss, the first to explicitly categorize pixels into three semantically distinct classes—edge, boundary, and texture—and assign class-specific supervision weights. EBT constitutes a structured generalization of WBCE, with theoretical proof confirming WBCE as its special case. Leveraging a differentiable three-class weighting scheme, EBT is framework-agnostic, requires no architecture modification, and supports consistent hyperparameters across models and datasets. Extensive experiments on multiple benchmarks demonstrate that EBT significantly improves edge localization accuracy and structural fidelity, outperforming state-of-the-art methods both quantitatively and visually. It exhibits strong robustness, cross-dataset generalizability, and plug-and-play practicality.
📝 Abstract
Edge detection (ED) remains a fundamental task in computer vision, yet its performance is often hindered by the ambiguous nature of non-edge pixels near object boundaries. The widely adopted Weighted Binary Cross-Entropy (WBCE) loss treats all non-edge pixels uniformly, overlooking the structural nuances around edges and often resulting in blurred predictions. In this paper, we propose the Edge-Boundary-Texture (EBT) loss, a novel objective that explicitly divides pixels into three categories, edge, boundary, and texture, and assigns each a distinct supervisory weight. This tri-class formulation enables more structured learning by guiding the model to focus on both edge precision and contextual boundary localization. We theoretically show that the EBT loss generalizes the WBCE loss, with the latter becoming a limit case. Extensive experiments across multiple benchmarks demonstrate the superiority of the EBT loss both quantitatively and perceptually. Furthermore, the consistent use of unified hyperparameters across all models and datasets, along with robustness to their moderate variations, indicates that the EBT loss requires minimal fine-tuning and is easily deployable in practice.