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Related Experiment Video

Updated: Sep 10, 2025

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

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

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Enhancing YOLOv5 for Autonomous Driving: Efficient Attention-Based Object Detection on Edge Devices.

Mortda A A Adam1, Jules R Tapamo1

  • 1School of Engineering, Howard College Campus, University of KwaZulu-Natal, Durban 4041, South Africa.

Journal of Imaging
|August 27, 2025
PubMed
Summary

This study introduces lightweight object detection models for autonomous driving, enhancing YOLOv5s with attention mechanisms. The BaseECAx2 model offers efficient edge deployment, while BaseSE-ECA achieves high accuracy for critical vehicle detection tasks.

Keywords:
attention mechanismautonomous drivingedge deviceslightweight modelobject detectionvehicle detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Autonomous Systems

Background:

  • Object detection is crucial for autonomous driving safety and efficiency.
  • Deep learning models are effective but computationally expensive for edge devices.
  • There is a need for lightweight, high-performance object detection models.

Purpose of the Study:

  • To develop lightweight object detection models for real-time autonomous driving on edge devices.
  • To integrate advanced channel attention strategies (ECA, SE) into the YOLOv5s architecture.
  • To evaluate model performance on standard datasets like KITTI and BDD-100K.

Main Methods:

  • Utilized the YOLOv5s architecture as a base for lightweight object detection.
  • Integrated Efficient Channel Attention (ECA) and Squeeze-and-Excitation (SE) attention modules.
  • Trained and evaluated four distinct models on the KITTI and BDD-100K datasets.
  • Assessed performance using metrics such as precision, recall, and mean average precision (mAP).

Main Results:

  • BaseECAx2 model achieved the lowest GFLOPs (13) and smallest size (9.1 MB), ideal for edge devices.
  • BaseSE-ECA model demonstrated high accuracy with 96.69% precision and 98.4% mAP for vehicle detection.
  • Models showed reduced performance in challenging conditions (low-light, motion blur) on the BDD-100K dataset.

Conclusions:

  • Lightweight YOLOv5s models with attention mechanisms offer a balance of performance and efficiency for autonomous driving.
  • The BaseECAx2 and BaseSE-ECA models present cost-effective solutions for real-time edge deployment.
  • Further research is needed to improve robustness in complex, real-world driving scenarios.