Learning to utilize gradient information for crisp edge detection

Edge detection is a fundamental task in computer vision and it has made great progress under the development of deep convolutional neural networks (DCNNs), some of them have achieved a beyond human-level performance. However, recent top-performing edge detection methods tend to generate thick and blurred edge lines. In this work, we propose an effective method to solve this problem. Our approach consists of a lightweight pre-trained backbone, multi-scale contextual enhancement module aggregating gradient information (MCGI), boundary correction module (BCM), and boundary refinement module (BRM). In addition to this, we construct a novel hybrid loss function based on the Tversky index for solving the issue of imbalanced pixel distribution. We test our method on three standard benchmarks and the experiment results illustrate that our method improves the visual effect of edge maps and achieves a top performance among several state-of-the-art methods on the BSDS500 dataset (ODS F-score in standard evaluation is 0.829, in crispness evaluation is 0.720), NYUD-V2 dataset (ODS F-score in standard evaluation is 0.768, in crispness evaluation is \textbf{0.546}), and BIPED dataset (ODS F-score in standard evaluation is 0.903).