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A robust and high-precision edge segmentation and refinement method for high-resolution images.

Qiming Li¹, Chengcheng Chen¹

¹College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

Mathematical Biosciences and Engineering : MBE

|January 18, 2023

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High-Resolution Refine Net (HRRNet) enhances semantic segmentation for high-resolution images by combining rough and refinement modules. This approach improves accuracy and speed, outperforming existing models.

Area of Science:

Computer Vision
Deep Learning
Image Processing

Background:

High-resolution image segmentation is computationally intensive and limited by GPU memory.
Existing methods struggle to balance detail preservation and computational efficiency.

Purpose of the Study:

To propose a novel network architecture, High-Resolution Refine Net (HRRNet), for accurate and efficient high-resolution image segmentation.
To address the limitations of GPU memory and improve segmentation accuracy.

Main Methods:

HRRNet integrates a rough segmentation module (improving DeepLabV3+) and a refinement module.
The refinement module processes global context and local patches using cascaded Refine Units (RU) and Residual Convolutional Units (RCU).
Regional non-maximum suppression enhances Sobel edge detection; Pascal VOC 2012 dataset is enhanced.

Keywords:

Sobel operator cascading method data augmentation edge refinement global process and local process high-resolution semantic segmentation

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Main Results:

The proposed HRRNet achieves superior performance in high-resolution image segmentation compared to state-of-the-art models.
The network demonstrates improved mean Intersection over Union (mIoU) and faster convergence.
Enhanced segmentation accuracy and robust performance are observed.

Conclusions:

HRRNet effectively overcomes GPU memory limitations for high-resolution image segmentation.
The dual-module architecture and refined processing units significantly boost segmentation quality.
HRRNet represents a significant advancement in semantic segmentation for high-resolution imagery.