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Open Access

ARTICLE

PIDINet-MC: Real-Time Multi-Class Edge Detection with PiDiNet

Mingming Huang1, Yunfan Ye1,*, Zhiping Cai2
1 School of Design, Hunan University, Changsha, 410082, China
2 College of Computer, National University of Defense Technology, Changsha, 410082, China
* Corresponding Author: Yunfan Ye. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.072399

Received 26 August 2025; Accepted 17 October 2025; Published online 18 November 2025

Abstract

As a fundamental component in computer vision, edges can be categorized into four types based on discontinuities in reflectance, illumination, surface normal, or depth. While deep CNNs have significantly advanced generic edge detection, real-time multi-class semantic edge detection under resource constraints remains challenging. To address this, we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection. Our model simultaneously predicts background and four edge categories from full-resolution inputs, balancing accuracy and efficiency. Key contributions include: a multi-channel output structure expanding binary edge prediction to five classes, supported by a deep supervision mechanism; a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance; and maintained architectural efficiency enabling real-time inference. Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.

Keywords

Multi-class edge detection; real-time; lightweight; deep supervision

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