Learning Crisp Boundaries Using Deep Refinement Network and Adaptive Weighting Loss
This work provides an incremental improvement in boundary detection precision for computer vision applications.
This paper addresses the challenge of detecting "crisp" boundaries in images, which are precisely localized along object contours. The authors propose a Deep Refinement Network (DRNet) and an adaptive weighting loss function, achieving state-of-the-art performance on several datasets.
Significant progress has been made in boundary detection with the help of convolutional neural networks. Recent boundary detection models not only focus on real object boundary detection but also "crisp" boundaries (precisely localized along the object's contour). There are two methods to evaluate crisp boundary performance. One uses more strict tolerance to measure the distance between the ground truth and the detected contour. The other focuses on evaluating the contour map without any postprocessing. In this study, we analyze both methods and conclude that both methods are two aspects of crisp contour evaluation. Accordingly, we propose a novel network named deep refinement network (DRNet) that stacks multiple refinement modules to achieve richer feature representation and a novel loss function, which combines cross-entropy and dice loss through effective adaptive fusion. Experimental results demonstrated that we achieve state-of-the-art performance for several available datasets.