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Updated: Jan 13, 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 Structurally Optimized and Efficient Lightweight Object Detection Model for Autonomous Driving.

Mingjing Li¹, Junshuai Wang¹, Shuang Chen²

¹College of Electronic Information Engineering, Changchun University, Changchun 130022, China.

Sensors (Basel, Switzerland)

|January 10, 2026

Summary

This summary is machine-generated.

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See all related articles

FE-YOLOv8 offers a lightweight object detection solution by introducing novel C2f-Faster and EfficientHead modules. This enhances efficiency and accuracy for safety-critical applications like autonomous driving.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Object detection is crucial for safety-critical systems like autonomous driving.
Current state-of-the-art detectors are often resource-intensive, posing challenges for efficiency.
Balancing accuracy and computational cost in object detection remains a significant research problem.

Purpose of the Study:

To propose FE-YOLOv8, a lightweight and effective variant of YOLOv8 (You Only Look Once version 8).
To address the accuracy-efficiency trade-off in resource-constrained object detection.
To introduce architectural innovations for improved lightweight object detector design.

Main Methods:

Introduced C2f-Faster modules with partial convolution (PConv) in the backbone and neck.

Keywords:

C2f-Faster EMSConv YOLOv8 autonomous driving lightweight design object detection

Related Experiment Videos

Last Updated: Jan 13, 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

Developed an EfficientHead detection head utilizing Efficient Multi-Scale Convolution (EMSConv).

Conducted ablation and comparative experiments on SODA-10M and BDD100K datasets.

Main Results:

FE-YOLOv8 achieved a 31.09% reduction in parameter count and a 43.31% decrease in computational cost compared to baseline YOLOv8.
Maintained comparable or superior mean Average Precision (mAP) on the SODA-10M dataset.
Demonstrated strong generalization performance on the BDD100K dataset.

Conclusions:

FE-YOLOv8 successfully mitigates the accuracy-efficiency trade-off in object detection.
The proposed architectural refinements offer valuable insights for designing efficient lightweight object detectors.
FE-YOLOv8 presents a promising solution for deploying object detection in resource-limited safety-critical applications.