Learning to utilize image second-order derivative information for crisp edge detection

Edge detection is a fundamental task in computer vision. It has made great progress under the development of deep convolutional neural networks (DCNNs), some of which have achieved a beyond human-level performance. However, recent top-performing edge detection methods tend to generate thick and noisy edge lines. In this work, we solve this problem from two aspects: (1) leveraging the precise edge pixel location characteristics of second-order image derivatives, and (2) alleviating the issue of imbalanced pixel distribution. We propose a second-order derivative-based multi-scale contextual enhancement module (SDMC) to help the model locate true edge pixels accurately and construct a hybrid focal loss function (HFL) to alleviate the imbalanced distribution issue. We test our method on three standard benchmarks and the experiment results illustrate that our method can make the output edge maps crisp 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 0.546), and BIPED dataset (ODS F-score in standard evaluation is 0.903).