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

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.