Richer Convolutional Features for Edge Detection (1612.02103v3)

Abstract: In this paper, we propose an accurate edge detector using richer convolutional features (RCF). Since objects in nature images have various scales and aspect ratios, the automatically learned rich hierarchical representations by CNNs are very critical and effective to detect edges and object boundaries. And the convolutional features gradually become coarser with receptive fields increasing. Based on these observations, our proposed network architecture makes full use of multiscale and multi-level information to perform the image-to-image edge prediction by combining all of the useful convolutional features into a holistic framework. It is the first attempt to adopt such rich convolutional features in computer vision tasks. Using VGG16 network, we achieve \sArt results on several available datasets. When evaluating on the well-known BSDS500 benchmark, we achieve ODS F-measure of \textbf{.811} while retaining a fast speed (\textbf{8} FPS). Besides, our fast version of RCF achieves ODS F-measure of \textbf{.806} with \textbf{30} FPS.

• The paper presents RCF, a CNN architecture that integrates hierarchical features for significantly improved edge detection precision.
• It introduces an annotator-robust loss function to effectively manage the ambiguities in human-labeled edge datasets.
• RCF employs multiscale testing, achieving state-of-the-art performance on benchmarks like BSDS500 and NYUD while maintaining computational efficiency.

Richer Convolutional Features for Edge Detection

In the paper "Richer Convolutional Features for Edge Detection," Liu et al. propose a novel method for edge detection utilizing Convolutional Neural Networks (CNNs). The principal innovation presented in the paper is a novel network architecture, termed Richer Convolutional Features (RCF), which comprehensively leverages the hierarchical features produced by all convolutional layers in a deep CNN, specifically VGG16. This comprehensive approach improves the ability to predict edges with high precision while maintaining efficiency.

Key Contributions

1. Integration of All Convolutional Layers: Unlike previous approaches that utilize only the final layer or select layers before pooling, RCF integrates features from each convolutional layer in VGG16. This integration allows RCF to capture both high-level semantic information and low-level fine details, leading to improved edge detection.
2. Novel Loss Function: The authors propose an annotator-robust loss function designed to handle the inherent ambiguities present in human-labeled edge datasets. This function weights positive and negative samples appropriately and disregards confusing samples, which enhances the training process.
3. Multiscale Detection: RCF also supports multiscale testing, a process by which images are resized at different scales to capture edges more accurately. This multiscale procedure improves the ODS F-measure, reflecting a better balance between precision and recall.

Numerical Results and Comparisons

The RCF network exhibits state-of-the-art performance on several benchmarks. On the BSDS500 dataset, RCF achieves an ODS F-measure of 0.811, which outperforms human perception (ODS F-measure of 0.803). A faster version of RCF achieves an ODS F-measure of 0.806 while maintaining a frame rate of 30 FPS, demonstrating that the method is not only accurate but also computationally efficient. Importantly, when using deeper networks like ResNet50 and ResNet101, the performance is further enhanced, reaching an ODS F-measure of 0.819.

On the NYUD dataset, which includes RGB-D information, RCF outperforms existing methods significantly. The combined use of RGB and HHA (horizontal disparity, height above ground, and angle with gravity) features leads to an ODS F-measure of 0.765, which is higher compared to the best-performing existing methods.

Practical and Theoretical Implications

The implications of this research are manifold. Practically, the superior performance and efficiency of RCF make it a robust choice for real-time edge detection applications. The method's strong performance in various contexts, including stereo matching, object detection, and image segmentation, demonstrates its versatility.

Theoretically, the approach of utilizing richer convolutional features underscores the importance of leveraging a CNN's full hierarchical structure. This paper also suggests the potential for applying such architectures beyond edge detection to other vision tasks like semantic segmentation and skeleton detection.

Future Directions

Future research could explore several avenues based on the findings of this paper. One potential development is the application of RCF in more diverse and complex datasets, possibly integrating additional modalities beyond RGB and depth. Further explorations could also consider optimizing network architectures to further enhance efficiency and accuracy, especially in edge cases where rapid processing and high precision are critical.

The presented method provides a valuable contribution to the field of edge detection, opening avenues for enhancing various computer vision tasks that rely heavily on accurate edge and boundary information.