Pixel Difference Networks for Efficient Edge Detection (2108.07009v1)

Abstract: Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level performance in edge detection with the rich and abstract edge representation capacities. However, the high performance of CNN based edge detection is achieved with a large pretrained CNN backbone, which is memory and energy consuming. In addition, it is surprising that the previous wisdom from the traditional edge detectors, such as Canny, Sobel, and LBP are rarely investigated in the rapid-developing deep learning era. To address these issues, we propose a simple, lightweight yet effective architecture named Pixel Difference Network (PiDiNet) for efficient edge detection. Extensive experiments on BSDS500, NYUD, and Multicue are provided to demonstrate its effectiveness, and its high training and inference efficiency. Surprisingly, when training from scratch with only the BSDS500 and VOC datasets, PiDiNet can surpass the recorded result of human perception (0.807 vs. 0.803 in ODS F-measure) on the BSDS500 dataset with 100 FPS and less than 1M parameters. A faster version of PiDiNet with less than 0.1M parameters can still achieve comparable performance among state of the arts with 200 FPS. Results on the NYUD and Multicue datasets show similar observations. The codes are available at https://github.com/zhuoinoulu/pidinet.

• The paper introduces Pixel Difference Convolutions that fuse traditional edge operators with CNNs for efficient edge detection.
• It presents a lightweight architecture using residual and separable convolutions to achieve real-time performance with minimal parameters.
• Experimental results on BSDS500 and other benchmarks demonstrate that PiDiNet outperforms state-of-the-art methods while reducing computational costs.

Pixel Difference Networks for Efficient Edge Detection

The paper "Pixel Difference Networks for Efficient Edge Detection" presents a novel approach to edge detection in computer vision, aiming to reduce the computational and memory burdens associated with deep CNN architectures while integrating traditional edge detection methods. This essay provides an expert analysis of the methodology, results, and implications of the research.

Background and Motivation

Edge detection is a foundational problem in computer vision, crucial for tasks like object recognition, segmentation, and proposal generation. Traditional methods, such as Canny and Sobel, focus on gradient information but lack the depth and abstraction capabilities of CNNs. Recent CNN-based approaches have significantly improved edge detection accuracy but often require large, pretrained backbones, resulting in high memory and energy consumption. This paper addresses these challenges by proposing a lightweight architecture that leverages both traditional and modern techniques.

Pixel Difference Networks (PiDiNet)

PiDiNet introduces pixel difference convolutions (PDC) to capture image gradient information effectively. PDCs integrate traditional edge detection operators within convolutional operations, enhancing CNNs' ability to detect edges more accurately and efficiently. Several PDC instances are derived, including Central, Angular, and Radial PDCs, each focusing on different pixel pair selection strategies. These PDCs help in maintaining the powerful learning capabilities of CNNs while explicitly focusing on edge-relevant features.

Architectural Design and Efficiency

The PiDiNet architecture is structured to maximize efficiency and accuracy. It uses a lightweight backbone with residual connections and separable depth-wise convolutions. Additionally, it incorporates compact dilation convolution modules (CDCM) and compact spatial attention modules (CSAM) to further refine the network's feature extraction capabilities. The design enables PiDiNet to perform at human-level edge detection rates with significantly reduced parameters and computational overhead.

Experimental Results

Comprehensive experiments on datasets such as BSDS500, NYUD, and Multicue demonstrate PiDiNet's efficacy. Remarkably, PiDiNet achieves an ODS F-measure of 0.807 on the BSDS500 dataset, surpassing human perception, with a model running at 100 FPS and fewer than 1M parameters. Even a smaller PiDiNet variant, with less than 0.1M parameters, maintains competitive performance at 200 FPS. These results showcase the model's capability to deliver high accuracy with a fraction of the computational cost compared to state-of-the-art methods.

Implications and Future Directions

The integration of traditional edge detection algorithms within CNNs, as proposed by this research, opens up avenues for developing more efficient neural networks that can be both lightweight and highly performant. Practically, such architectures can be deployed in real-time applications on edge devices, expanding the accessibility of advanced computer vision technologies.

Theoretically, the demonstrated approach invites further exploration into hybrid models that blend handcrafted features with learned representations. Future work could extend PiDiNet’s application to more complex vision tasks, such as multi-object detection and semantic segmentation, potentially setting new benchmarks for performance and efficiency.

The research presents quantifiable improvements in edge detection while addressing computational inefficiencies, marking a substantial contribution to the field. This work exemplifies how traditional techniques can be revitalized within modern frameworks to achieve state-of-the-art results with reduced resource requirements.