Edge detection (ED) is a fundamental perceptual process in computer vision, forming the structural basis for high-level reasoning tasks such as segmentation, recognition, and scene understanding. Despite substantial progress achieved by deep neural networks, most ED models attain high numerical accuracy but fail to produce visually sharp and perceptually consistent edges, thereby limiting their reliability in intelligent vision systems. To address this issue, this study introduces the Symmetrization Weighted Binary Cross-Entropy (SWBCE) loss, a perception-inspired formulation that extends the conventional WBCE by incorporating prediction-guided symmetry. SWBCE explicitly models the perceptual asymmetry in human edge recognition, wherein edge decisions require stronger evidence than non-edge ones, aligning the optimization process with human perceptual discrimination. The resulting symmetric learning mechanism jointly enhances edge recall and suppresses false positives, achieving a superior balance between quantitative accuracy and perceptual fidelity. Extensive experiments across multiple benchmark datasets and representative ED architectures demonstrate that SWBCE can outperform existing loss functions in both numerical evaluation and visual quality. Particularly with the HED-EES model, the SSIM can be improved by about 15% on BRIND, and in all experiments, training by SWBCE consistently obtains the best perceptual results. Beyond edge detection, the proposed perceptual loss offers a generalizable optimization principle for soft computing and neural learning systems, particularly in scenarios where asymmetric perceptual reasoning plays a critical role.

翻译：边缘检测是计算机视觉中的基础感知过程，为分割、识别和场景理解等高级推理任务提供结构基础。尽管深度神经网络已取得显著进展，但大多数边缘检测模型虽能获得较高的数值精度，却无法生成视觉清晰且感知一致的边缘，从而限制了其在智能视觉系统中的可靠性。为解决这一问题，本研究提出对称化加权二元交叉熵损失函数——一种受感知启发的公式化方法，通过引入预测引导的对称性扩展了传统加权二元交叉熵。该方法显式建模了人类边缘识别中的感知不对称性（边缘决策比非边缘决策需要更强证据），使优化过程与人类感知判别机制保持一致。由此产生的对称学习机制共同提升了边缘召回率并抑制了误报，在数值精度与感知保真度之间实现了更优平衡。在多个基准数据集和代表性边缘检测架构上的大量实验表明，SWBCE在数值评估和视觉质量方面均优于现有损失函数。特别是在HED-EES模型中，BRIND数据集的SSIM指标可提升约15%，且所有实验中采用SWBCE训练均能获得最佳感知结果。除边缘检测外，所提出的感知损失为软计算和神经学习系统提供了可推广的优化原则，尤其适用于感知不对称推理起关键作用的场景。