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Related Experiment Video

Updated: Apr 21, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

A triple-branch multi-scale network for real-time semantic segmentation.

Yuefeng Zhao¹, Guicong Zhang¹, Nannan Hu²

¹School of Physics and Electronics, Shandong Normal University, Jinan, 250300, China.

Scientific Reports

|April 19, 2026

Summary

This summary is machine-generated.

We introduce the Triple-Branch Multi-Scale Network (TBMSNet) to enhance real-time semantic segmentation accuracy. Our novel approach improves feature extraction and receptive field size, achieving a superior balance between speed and precision.

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Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Multi-scale learning is crucial for real-time semantic segmentation accuracy.
Existing methods neglect the impact of CNN receptive fields on feature discriminability, leading to limitations.

Purpose of the Study:

To propose a novel Triple-Branch Multi-Scale Network (TBMSNet) for improved real-time semantic segmentation.
To address limitations of small receptive fields and insufficient feature extraction in current multi-scale methods.

Main Methods:

Introduced the Simple Inverted Residual (SIR) module for enhanced initial feature extraction.
Designed a triple-branch structure to process detail, semantic, and boundary information separately.
Developed Dilation-wise Residual (DWR) and Multi-scale Semantic Aggregation Pyramid Pooling (MSAPP) modules to improve feature extraction and multi-scale information capture.

Main Results:

TBMSNet achieved 80.5% mIoU accuracy on the Cityscapes dataset.
The network demonstrated an inference speed of 50.4 FPS.
Achieved a state-of-the-art trade-off between inference speed and accuracy for real-time semantic segmentation.

Conclusions:

The proposed TBMSNet effectively enhances feature extraction and receptive field size.
TBMSNet offers a significant improvement in real-time semantic segmentation performance.
The novel architecture provides a superior balance between accuracy and speed.