Rethinking Edge Detection through Perceptual Asymmetry: The SWBCE Loss

Abstract: Edge detection (ED) is a fundamental component in many computer vision tasks, yet achieving both high quantitative accuracy and perceptual quality remains a significant challenge. In this paper, we propose the Symmetrization Weighted Binary Cross-Entropy (SWBCE) loss function, a novel approach that addresses this issue by leveraging the inherent asymmetry in human edge perception, where edge decisions require stronger justification than non-edge ones. By balancing label-guided and prediction-guided learning, SWBCE maintains high edge recall while effectively suppressing false positives. Extensive experiments across multiple datasets and baseline models, along with comparisons to prior loss functions, demonstrate that our method consistently improves both the quantitative metrics and perceptual quality of ED results. These findings underscore the effectiveness of SWBCE for high-quality edge prediction and its potential applicability to related vision tasks.