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Image Semantic Segmentation Method Based on Deep Fusion Network and Conditional Random Field.

Shuo Wang¹, Yi Yang²

¹School of Energy and Intelligence Engineering, Henan University of Animal Husbandry and Economy, Zhengzhou, Henan 450044, China.

Computational Intelligence and Neuroscience

|May 24, 2022

Summary

This study introduces an improved image semantic segmentation method using a deep fusion network and conditional random fields. The novel approach enhances accuracy for complex backgrounds and small targets, outperforming existing techniques.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Image semantic segmentation faces challenges with complex backgrounds and small targets, leading to missing or incorrect segmentation points.
Existing methods often struggle to effectively fuse multi-scale features and refine segmentation details.

Purpose of the Study:

To propose an advanced image semantic segmentation method addressing limitations in complex scenes and small object detection.
To improve segmentation accuracy by effectively integrating shallow details and deep semantic information.

Main Methods:

A deep fusion network incorporating a deconvolution fusion structure was developed to automatically extract multi-scale features.
Shallow detail and deep semantic information were fused to enhance rough segmentation accuracy.
A conditional random field with an optimized bivariate potential function was employed for fine segmentation.

Main Results:

The proposed method demonstrated accurate image segmentation capabilities on the Cityscapes dataset.
Achieved an area under the segmentation curve of 93.6% for overall size targets.
Outperformed other existing methods in segmentation performance.

Conclusions:

The developed deep fusion network and conditional random field approach effectively improves image semantic segmentation.
The method shows significant promise for applications requiring precise segmentation of complex scenes and small objects.