Learning to predict crisp boundaries (1807.10097v1)

Abstract: Recent methods for boundary or edge detection built on Deep Convolutional Neural Networks (CNNs) typically suffer from the issue of predicted edges being thick and need post-processing to obtain crisp boundaries. Highly imbalanced categories of boundary versus background in training data is one of main reasons for the above problem. In this work, the aim is to make CNNs produce sharp boundaries without post-processing. We introduce a novel loss for boundary detection, which is very effective for classifying imbalanced data and allows CNNs to produce crisp boundaries. Moreover, we propose an end-to-end network which adopts the bottom-up/top-down architecture to tackle the task. The proposed network effectively leverages hierarchical features and produces pixel-accurate boundary mask, which is critical to reconstruct the edge map. Our experiments illustrate that directly making crisp prediction not only promotes the visual results of CNNs, but also achieves better results against the state-of-the-art on the BSDS500 dataset (ODS F-score of .815) and the NYU Depth dataset (ODS F-score of .762).

• The paper introduces a novel loss function based on the Dice coefficient to address class imbalance in edge detection tasks.
• It employs an end-to-end network with a bottom-up/top-down pathway inspired by U-Net to fuse hierarchical features for refined edge predictions.
• Experimental evaluations achieve state-of-the-art F-scores of 0.815 on BSDS500 and 0.762 on NYU Depth, eliminating the need for post-processing.

Review: Learning to Predict Crisp Boundaries

This paper presents a novel approach to detecting crisp boundaries in images using deep learning, sidestepping the post-processing steps often required traditionally. The authors focus on addressing the challenge of thick predicted edges, a common issue in CNN-based edge detection. By introducing a new loss function, the work effectively tackles the data imbalance problem in edge detection tasks, allowing for crisp boundary evaluations efficiently.

Summary of the Methodology

The core contribution of this paper is the introduction of a novel loss function specifically tailored for imbalanced data in edge detection tasks. This loss function builds upon the Dice coefficient, traditionally used to handle high class imbalance scenarios. The loss formulation seeks to directly maximize the overlap between predicted and true edges, circumventing the complications arising from the thicker edges produced by previous models.

The authors also propose an end-to-end network architecture that adopts a bottom-up/top-down pathway. This structure is inspired by the U-Net and other similar architectures that have shown success in dense prediction tasks. The network’s architecture efficiently leverages hierarchical features through a refined fusion strategy, minimizing the ambiguity in edge predictions and producing pixel-accurate boundary maps.

Experimental Results

The proposed method's performance is extensively evaluated on the BSDS500 and NYU Depth datasets. The results indicate that the method achieves state-of-the-art F-scores, with an ODS F-score of 0.815 on the BSDS500 dataset and 0.762 on the NYU Depth dataset. The quantitative results are complemented by qualitative examples showcasing the sharpness and precision of the predicted edges.

Implications and Future Development

Practically, the successful implementation of this method removes the necessity for cumbersome post-processing steps typically required to refine boundaries detected by CNNs, streamlining the integration of boundary detection into larger image processing or computer vision pipelines. The theoretical contributions primarily enhance the understanding of loss formulations in deep learning, specifically how tailored loss functions can alleviate class imbalance issues effectively.

Looking forward, the insights from this research could be applied to other dense prediction tasks beyond edge detection; this includes semantic segmentation, optical flow, and more. Additionally, the improved efficiency and precision of edge detection, as exhibited in this work, could potentially benefit applications requiring real-time processing, such as autonomous vehicles and robotic vision systems.

Conclusively, this paper provides a valuable step forward in precise edge detection by proposing an innovative solution to a classic problem in computer vision, thereby advancing both the theory and practical capabilities of edge-detection technologies.