Open Access

ARTICLE

An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

by Ruobing Zuo¹, Xiaohan Huang¹, Xuguo Jiao^2,3, Zhenyong Zhang^1,4,5,*

1 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China
2 School of Information and Control Engineering, Qingdao University of Technology, Qingdao, 266520, China
3 State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China
4 Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China
5 Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry, Guizhou University, Guiyang, 550025, China

* Corresponding Author: Zhenyong Zhang. Email:

Computers, Materials & Continua 2024, 80(2), 3333-3349. https://doi.org/10.32604/cmc.2024.050544

Received 09 February 2024; Accepted 08 April 2024; Issue published 15 August 2024

Abstract

In the rapidly evolving urban landscape, outdoor parking lots have become an indispensable part of the city’s transportation system. The growth of parking lots has raised the likelihood of spontaneous vehicle combustion, a significant safety hazard, making smoke detection an essential preventative step. However, the complex environment of outdoor parking lots presents additional challenges for smoke detection, which necessitates the development of more advanced and reliable smoke detection technologies. This paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking lots. First, we develop a novel dataset to fill the gap, as there is a lack of publicly available data. This dataset encompasses a wide range of smoke and fire scenarios, enhanced with data augmentation to ensure robustness against diverse outdoor conditions. Second, we utilize an optimized YOLOv5s model, integrated with the Squeeze-and-Excitation Network (SENet) attention mechanism, to significantly improve detection accuracy while maintaining real-time processing capabilities. Third, this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time, enhancing the effectiveness and reliability of emergency response. Experiments show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

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Keywords

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