PEdger++: Practical Edge Detection via Assembling Cross Information

TL;DR

PEdger++ is a collaborative learning framework that enhances edge detection accuracy while reducing computational costs, making it suitable for deployment on devices with varying resources.

Contribution

It introduces a novel ensemble-inspired approach leveraging cross-information from heterogeneous models to improve efficiency and accuracy in edge detection.

Findings

Outperforms existing methods on BSDS500, NYUD, and Multicue datasets.

Provides multiple model versions for different computational budgets.

Demonstrates significant accuracy improvements with reduced model sizes.

Abstract

Edge detection serves as a critical foundation for numerous computer vision applications, including object detection, semantic segmentation, and image editing, by extracting essential structural cues that define object boundaries and salient edges. To be viable for broad deployment across devices with varying computational capacities, edge detectors shall balance high accuracy with low computational complexity. While deep learning has evidently improved accuracy, they often suffer from high computational costs, limiting their applicability on resource-constrained devices. This paper addresses the challenge of achieving that balance: \textit{i.e.}, {how to efficiently capture discriminative features without relying on large-size and sophisticated models}. We propose PEdger++, a collaborative learning framework designed to reduce computational costs and model sizes while improving edge detection accuracy. The core principle of our PEdger++ is that cross-information derived from heterogeneous architectures, diverse training moments, and multiple parameter samplings, is beneficial to enhance learning from an ensemble perspective. Extensive experimental results on the BSDS500, NYUD and Multicue datasets demonstrate the effectiveness of our approach, both quantitatively and qualitatively, showing clear improvements over existing methods. We also provide multiple versions of the model with varying computational requirements, highlighting PEdger++'s adaptability with respect to different resource constraints. Codes are accessible at https://github.com/ForawardStar/EdgeDetectionviaPEdgerPlus/.