Edge detection is crucial in medical image processing, enabling precise extraction of structural information to support lesion identification and image analysis. Traditional edge detection models typically rely on complex Convolutional Neural Networks and Vision Transformer architectures. Due to their numerous parameters and high computational demands, these models are limited in their application on resource-constrained devices. This paper presents an ultra-lightweight edge detection model (UHNet), characterized by its minimal parameter count, rapid computation speed, negligible of pre-training costs, and commendable performance. UHNet boasts impressive performance metrics with 42.3k parameters, 166 FPS, and 0.79G FLOPs. By employing an innovative feature extraction module and optimized residual connection method, UHNet significantly reduces model complexity and computational requirements. Additionally, a lightweight feature fusion strategy is explored, enhancing detection accuracy. Experimental results on the BSDS500, NYUD, and BIPED datasets validate that UHNet achieves remarkable edge detection performance while maintaining high efficiency. This work not only provides new insights into the design of lightweight edge detection models but also demonstrates the potential and application prospects of the UHNet model in engineering applications such as medical image processing. The codes are available at https://github.com/stoneLi20cv/UHNet

翻译：边缘检测在医学图像处理中至关重要，能够精确提取结构信息以支持病灶识别与图像分析。传统边缘检测模型通常依赖复杂的卷积神经网络和视觉Transformer架构。由于参数量大、计算需求高，这些模型在资源受限设备上的应用受到限制。本文提出一种超轻量边缘检测模型（UHNet），其特点在于参数量极少、计算速度快、预训练成本可忽略不计且性能表现优异。UHNet具有42.3k参数量、166 FPS和0.79G FLOPs的卓越性能指标。通过采用创新的特征提取模块和优化的残差连接方法，UHNet显著降低了模型复杂度和计算需求。此外，本文探索了轻量级特征融合策略，提升了检测精度。在BSDS500、NYUD和BIPED数据集上的实验结果验证了UHNet在保持高效率的同时实现了显著的边缘检测性能。这项工作不仅为轻量级边缘检测模型设计提供了新思路，也展现了UHNet模型在医学图像处理等工程应用中的潜力与应用前景。代码已开源：https://github.com/stoneLi20cv/UHNet