Bi-Directional Cascade Network for Perceptual Edge Detection (1902.10903v1)

Abstract: Exploiting multi-scale representations is critical to improve edge detection for objects at different scales. To extract edges at dramatically different scales, we propose a Bi-Directional Cascade Network (BDCN) structure, where an individual layer is supervised by labeled edges at its specific scale, rather than directly applying the same supervision to all CNN outputs. Furthermore, to enrich multi-scale representations learned by BDCN, we introduce a Scale Enhancement Module (SEM) which utilizes dilated convolution to generate multi-scale features, instead of using deeper CNNs or explicitly fusing multi-scale edge maps. These new approaches encourage the learning of multi-scale representations in different layers and detect edges that are well delineated by their scales. Learning scale dedicated layers also results in compact network with a fraction of parameters. We evaluate our method on three datasets, i.e., BSDS500, NYUDv2, and Multicue, and achieve ODS Fmeasure of 0.828, 1.3% higher than current state-of-the art on BSDS500. The code has been available at https://github.com/pkuCactus/BDCN.

• The paper presents a novel bi-directional cascade network that applies layer-specific supervision to effectively capture distinct edge scales.
• It employs a Scale Enhancement Module using dilated convolution to generate multi-scale features without relying on deeper CNN architectures.
• Performance evaluations show a 1.3% improvement in the ODS F-measure on benchmark datasets, confirming enhanced accuracy and computational efficiency.

Analyzing Bi-Directional Cascade Network for Perceptual Edge Detection

The paper "Bi-Directional Cascade Network for Perceptual Edge Detection" introduces an innovative approach to edge detection in images through a method known as the Bi-Directional Cascade Network (BDCN). The authors propose a novel framework characterized by both its attention to multi-scale edge detection and a new training strategy aimed at enhancing the network's efficiency and accuracy.

Overview of the Bi-Directional Cascade Network

The primary objective of BDCN is to enhance edge detection by addressing the varying scales of edges inherent in natural images. Traditional neural network approaches often struggle to efficiently capture edges at different scales without resorting to complex and computationally expensive models. BDCN mitigates this by integrating a bi-directional cascade architecture, facilitating supervised learning at each layer, focused on specific edge scales rather than a one-size-fits-all method.

BDCN's architecture leverages a Scale Enhancement Module (SEM) which employs dilated convolution to effectively generate multi-scale features without necessitating deeper CNN architectures or explicit fusion of multi-scale edge maps. SEM's role is crucial as it ensures that the network consolidates its ability to learn and represent features at various scales more compactly and efficiently.

Performance and Results

The authors validate BDCN's effectiveness by testing it on three datasets: BSDS500, NYUDv2, and Multicue. The numerical results demonstrate significant improvements over previous state-of-the-art techniques, highlighting a 1.3% increase in the ODS F-measure on the BSDS500 dataset, achieving an impressive 0.828. This improved performance underscores the model's ability to detect edges more accurately across varying scales without the extensive resource requirements of deeper models.

Implications and Future Work

The implications of this research are both practical and theoretical. Practically, BDCN promises a more efficient tool for edge detection in image processing tasks, making it particularly beneficial in resource-constrained environments or applications where computational efficiency is paramount. Theoretically, this work advances our understanding of multi-scale representations in neural networks and presents a robust training methodology that could inform future innovation in the field.

The contribution also suggests pathways for future work, including adaptation for real-time applications and further exploration of convolutional architecture variations. The compactness of the model implied by the lower parameter requirement suggests possible integrations into mobile or embedded computing environments. Additionally, the concept of scale-specific supervision could be extrapolated to other domains within computer vision and beyond.

The BDCN framework represents a shift in edge detection methodologies, fostering significant improvements through its unique architectural and methodological innovations. With this groundwork laid, the research community may build upon these insights to continue advancing the capabilities of artificial intelligence in visual perception tasks.