Efficient Stage Features for Edge Detection

Edge detection is a fundamental task in machine vision that facilitates feature extraction and representation across various visual domains, such as panoptic segmentation, autonomous driving, and image recognition. Despite the superior performance of current neural network-based edge detectors, the large parameter size renders edge detection models unsuitable for direct application in complex scenarios. Consequently, designing a compact edge detection network remains an imperative challenge. In this paper, we introduce the Efficient Stage Features Edge Detector (ESFED), a low-parameter, high-performance edge detector. ESFED is primarily composed of an efficient stage feature extractor, an upsampling network for edge features, and a feature fusion network for prediction, totaling only 51K parameters. It achieves 0.829 Optimal Dataset Scale (ODS) and 0.846 Optimal Image Scale (OIS) on the Unified Dataset for Edge Detection (UDED) dataset, demonstrating notable performance in comparison to other state-of-the-art models.