  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
