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Related Experiment Video

Updated: Mar 29, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

1.2K

SemOD: Semantic-Enabled Object Detection Network Under Various Weather Conditions.

Aiyinsi Zuo1, Zhaoliang Zheng2

  • 1Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA.

Sensors (Basel, Switzerland)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a semantic-enabled network for robust object detection in autonomous driving, improving performance across diverse weather conditions. The novel approach enhances image quality and perception, outperforming existing methods.

Keywords:
adverse weatherautonomous drivingobject detectionsemantic

Related Experiment Videos

Last Updated: Mar 29, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

1.2K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Current autonomous driving perception models often fail in adverse weather due to limited training data.
  • Existing methods focus on weather removal, not robust perception, limiting generalization in dynamic environments.

Purpose of the Study:

  • To propose a novel semantic-enabled network for accurate object detection under diverse weather conditions.
  • To enhance the generalization capabilities of autonomous driving perception systems.

Main Methods:

  • Developed a semantic-enabled network comprising a Preprocessing Unit (PPU) and a Detection Unit (DTU).
  • The PPU uses a U-shaped network with semantic enrichment to refine degraded images.
  • The DTU integrates semantic information into a modified YOLO network for object detection.

Main Results:

  • Achieved mean Average Precision (mAP) improvements from 1.49% to 8.78% across multiple benchmark datasets.
  • Demonstrated superior performance compared to existing approaches in various adverse weather conditions.
  • Validated the effectiveness of semantic guidance in both image enhancement and object detection.

Conclusions:

  • The proposed semantic-enabled network offers a comprehensive framework for improving object detection performance in autonomous driving.
  • Semantic information is crucial for robust perception and image restoration under challenging weather conditions.
  • The method provides a significant advancement for reliable autonomous vehicle operation in all weather scenarios.