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Related Experiment Video

Updated: Sep 1, 2025

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 Lightweight CNN Model Based on GhostNet.

Zhong Wang¹, Tong Li¹

¹School of Computer Science and Technology, Hefei Normal University, Hefei 230601, China.

Computational Intelligence and Neuroscience

|August 12, 2022

Summary

This summary is machine-generated.

This study introduces a lightweight deep learning model for efficient fire detection. The novel approach significantly improves detection speed and reduces model size, enabling deployment on resource-limited devices.

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Area of Science:

Computer Vision
Artificial Intelligence
Deep Learning

Background:

Existing deep learning models for fire detection suffer from large parameter counts and slow inference speeds.
These limitations hinder deployment on resource-constrained equipment, impacting practical applications like real-time fire detection.

Purpose of the Study:

To develop a lightweight smoke detection model that addresses the efficiency and resource limitations of current deep learning approaches.
To enhance the performance and speed of fire detection systems for practical, real-time engineering applications.

Main Methods:

A lightweight neural network model based on the YOLOv5 framework was proposed.
The model incorporates GhostNet design principles, GhostBottleNeck modules, and a convolutional attention mechanism.
The CIoU loss function was utilized to improve regression accuracy.

Main Results:

The proposed model achieved significantly lower parameter (2.75M) and computation (2.56G FLOPs) amounts compared to the YOLOv5s benchmark.
Experimental results on a public fire dataset demonstrated superior detection performance and notably faster detection speeds than traditional algorithms.
The model achieved a single-picture detection speed of 60ms on an unquantized simulator.

Conclusions:

The developed lightweight model offers a viable solution for efficient and accurate fire detection.
Its reduced size and enhanced speed make it suitable for deployment on edge devices with limited resources.
The model meets the real-time requirements for practical engineering applications in fire detection.