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Highlights • An edge detection network architecture based on deep convolutional network is presented. • A novel loss function is proposed to balance positive and negative samples. • The fast learning model is discovered (only 5.4k iteration) for edge detection. • The better generalization ability is verified at different dataset. Abstract In this paper, we propose an edge detector based on feature re-extraction (FRE) of a deep convolutional neural network to effectively utilize features extracted from each stage, and design a new loss function. The proposed detector is mainly composed of three modules: backbone, side-output, and feature fusion. The backbone module provides preliminary feature extraction; the side-output module makes network architecture more robustly map features from different stages of the backbone network to edge-pixel space by applying residual learning, and the feature fusion module generates the edge map. Generalization ability on the same distribution is verified using the BSDS500 dataset, achieving optimal dataset scale (ODS) F-score = 0.804. Cross-distribution generalization ability is verified on the NYUDv2 dataset, achieving ODS F-score = 0.701. In addition, we find that freezing backbone network can significantly speed up training process, without much overall accuracy loss (ODS F-score of 0.791 after 5.4k iterations). [ABSTRACT FROM AUTHOR]