Aiming at the multi-task optimization conflicts (structure-detail-denoising coupling) and high computational costs caused by existing edge detectors' reliance on complex pre-trained models, this paper proposes BCNet - the first innovative framework that synergistically integrates biological visual mechanisms and information theory to achieve triple decoupling. First, inspired by butterfly-shaped receptive fields in the visual system, we design learnable butterfly-shaped convolution kernels as fundamental operators. These kernels inherently enhance structural perception and suppress noise without requiring deep architectures and pre-training, achieving structure-denoising separation. Second, to address the detail-noise coupling issue in existing methods that reconstruct edge images from multi-scale downsampled features, we propose a conditional entropy-based uncertainty modeling approach. The uncertainty of feature loss during downsampling is quantified via Gaussian distributions, while a self-supervised mechanism dynamically assigns detaillearning weights, enabling the model to learn more details from high-uncertainty regions while avoiding noise introduction. With at most about 2M parameters and real-time inference speeds up to 152 FPS, BCNet demonstrates strong competitiveness across four benchmark datasets, providing a novel, efficient, and lightweight solution for edge detection.https://github.com/StarkLuo/BCNetCode will be available.