Edge Detection based on Channel Attention and Inter-region Independence Test

TL;DR

This paper introduces CAM-EDIT, a novel edge detection framework combining channel attention and independence testing to improve accuracy and noise robustness in industrial scenarios.

Contribution

It proposes a new edge detection method integrating channel attention and statistical independence testing, achieving state-of-the-art results and noise robustness.

Findings

Achieves higher F-measure scores than traditional and recent learning-based methods.

Demonstrates 2.2% PSNR improvement under Gaussian noise.

Produces cleaner edge maps with fewer artifacts.

Abstract

Existing edge detection methods often suffer from noise amplification and excessive retention of non-salient details, limiting their applicability in high-precision industrial scenarios. To address these challenges, we propose CAM-EDIT, a novel framework that integrates Channel Attention Mechanism (CAM) and Edge Detection via Independence Testing (EDIT). The CAM module adaptively enhances discriminative edge features through multi-channel fusion, while the EDIT module employs region-wise statistical independence analysis (using Fisher's exact test and chi-square test) to suppress uncorrelated noise.Extensive experiments on BSDS500 and NYUDv2 datasets demonstrate state-of-the-art performance. Among the nine comparison algorithms, the F-measure scores of CAM-EDIT are 0.635 and 0.460, representing improvements of 19.2\% to 26.5\% over traditional methods (Canny, CannySR), and better than the latest learning based methods (TIP2020, MSCNGP). Noise robustness evaluations further reveal a 2.2\% PSNR improvement under Gaussian noise compared to baseline methods. Qualitative results exhibit cleaner edge maps with reduced artifacts, demonstrating its potential for high-precision industrial applications.