Pixel Difference Unmixing Feature Networks for Edge Detection

The edge detection model based on deep learning significantly improves performance, but its generally high model complexity requires a large pretrained Convolutional Neural Networks (CNNs) backbone, and hence large memory and computing power. To solve this problem, we carefully choose proper components for edge detection, introduce a Multiscale Aware Fusion Module based on self-attention and a feature-unmixing loss function, and propose a lightweight network model, Pixel Difference Unmixing Feature Networks (PDUF). The backbone network of proposed model is designed to adopt skip long-short residual connection and does not use pre-trained weights, and requires straightforward hyper-parameter settings. Extensive experiments on the BSDS, NYUD, and Multi-cue datasets, we found that the proposed model has higher F-scores than current state-of-the-art lightweight models (those with fewer than 1 million parameters) on BSDS500 (ODS F-score of 0.818), NYUDv2 depth datasets (ODS F-score of 0.767) and Multi-Cue dataset (ODS F-score 0.871(0.002)), with similar performance compared with some large models (with about 35 million parameters).