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DrsNet: Dual-resolution Semantic Segmentation with Rare Class-Oriented Superpixel Prior.

Liangjiang Yu¹, Guoliang Fan¹

¹School of Electrical and Computer Engineering, Oklahoma State University, USA.

Multimedia Tools and Applications

|March 29, 2021

Summary

This study introduces a deep learning framework to improve rare-class object detection in natural scenes. By integrating shape information and a novel superpixel representation, it enhances semantic labeling accuracy for often-overlooked objects.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Rare-class objects in natural scenes are crucial for scene understanding but often overlooked in semantic labeling due to low frequency and limited spatial coverage.
Existing methods primarily focus on overall performance, neglecting the specific challenges posed by rare classes.

Purpose of the Study:

To develop a deep semantic labeling framework that specifically addresses the under-representation and detection challenges of rare-class objects.
To enhance the accuracy and robustness of semantic scene labeling, particularly for small and infrequent objects.

Main Methods:

A novel dual-resolution coarse-to-fine superpixel representation is employed, utilizing fine superpixels for rare classes and coarse superpixels for background areas.
Convolutional Neural Network (CNN) models are integrated with shape features, incorporating this information during both training (with re-balanced data) and inference.
A probabilistic multi-class likelihood fusion combines shape information and CNN architecture for improved semantic labeling.

Main Results:

The proposed framework demonstrates competitive semantic labeling performance on standard datasets, both qualitatively and quantitatively.
Significant improvements were observed in the accurate identification and labeling of rare-class objects compared to existing methods.
The integration of shape information and the dual-resolution superpixel representation proved effective in addressing the challenges of rare-class object detection.

Conclusions:

The developed deep semantic labeling framework effectively enhances the detection and labeling of rare-class objects in natural scenes.
The approach offers a promising solution for improving scene understanding by giving due attention to less frequent but important objects.
Future work could explore further refinements in feature fusion and superpixel generation for even greater accuracy.