Open Access

ARTICLE

Smoke Detector for Outdoor Parking Lots Based on Improved YOLOv8

Gang He¹, Zhuoyan Chen¹, Mufeng Wang², Xingcheng Yang³, Zhenyong Zhang^1,*

1 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China
2China Industrial Control Systems Cyber Emergency Response Team, Beijing, 100040, China
3 Fengtai Science and Technology (Beijing) Co., Ltd., Beijing, 100070, China

* Corresponding Author: Zhenyong Zhang. Email:

Computers, Materials & Continua 2025, 85(1), 729-750. https://doi.org/10.32604/cmc.2025.066748

Received 16 April 2025; Accepted 11 June 2025; Issue published 29 August 2025

Abstract

In rapid urban development, outdoor parking lots have become essential components of urban transportation systems. However, the increasing number of parking lots is accompanied by a rising risk of vehicle fires, posing a serious challenge to public safety. As a result, there is a critical need for fire warning systems tailored to outdoor parking lots. Traditional smoke detection methods, however, struggle with the complex outdoor environment, where smoke characteristics often blend into the background, resulting in low detection efficiency and accuracy. To address these issues, this paper introduces a novel model named Dynamic Contextual Transformer YOLO (DCT-YOLO), an advanced smoke detection method specifically designed for outdoor parking lots. We introduce an innovative Dynamic Channel-Spatial Attention (DCSA) mechanism to improve the model’s focus on smoke features, thus improving detection accuracy. Additionally, we incorporate Contextual Transformer Networks (CoTNet) to better adapt to the irregularity of smoke patterns, further enhancing the accuracy of smoke region detection in complex environments. Moreover, we developed a new dataset that includes a wide range of smoke and fire scenarios, improving the model’s generalization capability. All baseline models were trained and evaluated on the same dataset to ensure a fair and consistent comparison. The experimental results on this dataset demonstrate that the proposed algorithm yields a mAP@0.5 of 85.1% and a mAP@0.5:0.95 of 55.7%, representing improvements of 15.0% and 14.9%, respectively, over the baseline model. These results highlight the effectiveness of the proposed method in accurately detecting smoke in challenging outdoor environments.

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Keywords

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