Rethinking Edge Detection through Perceptual Asymmetry: The SWBCE Loss
This work addresses a fundamental problem in computer vision for applications requiring precise edge detection, though it appears incremental as it builds on existing loss functions.
The paper tackles the challenge of achieving both high quantitative accuracy and perceptual quality in edge detection by proposing the SWBCE loss function, which leverages human perceptual asymmetry to balance edge recall and false positive suppression, resulting in consistent improvements across multiple datasets and baseline models.
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.