UHNet: An Ultra-Lightweight and High-Speed Edge Detection Network

TL;DR

UHNet is a highly efficient, ultra-lightweight edge detection network designed for resource-constrained devices, achieving high speed and accuracy with minimal parameters and computational cost.

Contribution

The paper introduces UHNet, a novel lightweight edge detection model with innovative feature extraction and fusion strategies, significantly reducing complexity while maintaining high performance.

Findings

UHNet achieves 42.3k parameters and 166 FPS.

It demonstrates high accuracy on multiple datasets.

The model requires only 0.79G FLOPs.

Abstract

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