Image edge detection (ED) requires specialized architectures, reliable supervision, and rigorous evaluation criteria to ensure accurate localization. In this work, we present a framework for high-precision ED that jointly addresses architectural design, data supervision, and evaluation consistency. We propose SDPED, a compact ED model built upon Cascaded Skipping Density Blocks (CSDB), motivated by a task-adaptive architectural transfer from image super-resolution. By re-engineering texture-oriented structures for ED, SDPED effectively differentiates textures from edges while preserving fine spatial precision. Extensive experiments on four benchmark datasets (BRIND, UDED, MDBD, and BIPED2) demonstrate consistent performance improvements, particularly in Average Precision (AP), with gains of up to 22.5% on MDBD and 11.8% on BIPED2. In addition, we introduce an ideal-prior guidance strategy that incorporates noiseless data into training by treating labels as noise-free samples, providing a practical means to mitigate the subjectivity and noise inherent in human annotations. To enable fair and resolution-independent evaluation, we further adopt a fixed-pixel criterion for assessing localization accuracy. Overall, this work offers a coherent solution for high-precision ED and provides insights applicable to precision-oriented modeling in low-level and soft-computing-based vision tasks. Codes can be found on https://github.com/Hao-B-Shu/SDPED.

翻译：图像边缘检测需要专门的架构设计、可靠的监督信号以及严谨的评估标准，以确保精确定位。本文提出一个高精度边缘检测框架，从架构设计、数据监督与评估一致性三方面进行联合优化。我们提出了SDPED模型，该紧凑型边缘检测模型基于级联跳跃密度块构建，其设计灵感源于从图像超分辨率任务中迁移而来的任务自适应架构思想。通过将面向纹理的结构重新设计以适应边缘检测任务，SDPED能够有效区分纹理与边缘，同时保持精细的空间定位精度。在四个基准数据集（BRIND、UDED、MDBD与BIPED2）上的大量实验表明，该方法取得了持续的性能提升，尤其在平均精度指标上表现突出——在MDBD上提升达22.5%，在BIPED2上提升达11.8%。此外，我们提出一种理想先验引导策略，通过将标注视为无噪声样本，将纯净数据引入训练过程，为缓解人工标注中固有的主观性与噪声提供了实用途径。为实现公平且与分辨率无关的评估，我们进一步采用固定像素准则来评定定位精度。总体而言，本研究为高精度边缘检测提供了一个系统性的解决方案，其思路也可推广至低层视觉任务及基于软计算的视觉建模中。代码公开于：https://github.com/Hao-B-Shu/SDPED。