FIRE-YOLOv8s: A Lightweight and Efficient Algorithm for Tunnel Fire Detection Article Swipe
Lingyu Bu
,
Wenfeng Li
,
Hongmin Zhang
,
Hong Wang
,
Qianqian Tian
,
Yong Zhou
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/fire8040125
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/fire8040125
To address the challenges of high algorithmic complexity and low accuracy in current fire detection algorithms for highway tunnel scenarios, this paper proposes a lightweight tunnel fire detection algorithm, FIRE-YOLOv8s. First, a novel feature extraction module, P-C2f, is designed using partial convolution (PConv). By dynamically determining the convolution kernel’s range of action, the module significantly reduces the model’s computational load and parameter count. Additionally, the ADown module is introduced for downsampling, employing a lightweight and branching design to minimize computational requirements while preserving essential feature information. Secondly, the neck feature fusion network is redesigned using a lightweight CNN-based cross-scale fusion module (CCFF). This module leverages lightweight convolution operations to achieve efficient cross-scale feature fusion, further reducing model complexity and enhancing the fusion efficiency of multi-scale features. Finally, the dynamic head detection head is introduced, incorporating multiple self-attention mechanisms to better capture key information in complex scenes. This improvement enhances the model’s accuracy and robustness in detecting fire targets under challenging conditions. Experimental results on the self-constructed tunnel fire dataset demonstrate that, compared to the baseline model YOLOv8s, FIRE-YOLOv8s reduces the computational load by 47.2%, decreases the number of parameters by 52.2%, and reduces the model size to 50% of the original, while achieving a 4.8% improvement in accuracy and a 1.7% increase in [email protected]. Furthermore, deployment experiments on a tunnel emergency firefighting robot platform validate the algorithm’s practical applicability, confirming its effectiveness in real-world scenarios.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/fire8040125
- https://www.mdpi.com/2571-6255/8/4/125/pdf?version=1742821065
- OA Status
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- 35
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- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4408778354Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/fire8040125Digital Object Identifier
- Title
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FIRE-YOLOv8s: A Lightweight and Efficient Algorithm for Tunnel Fire DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-24Full publication date if available
- Authors
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Lingyu Bu, Wenfeng Li, Hongmin Zhang, Hong Wang, Qianqian Tian, Yong ZhouList of authors in order
- Landing page
-
https://doi.org/10.3390/fire8040125Publisher landing page
- PDF URL
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https://www.mdpi.com/2571-6255/8/4/125/pdf?version=1742821065Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2571-6255/8/4/125/pdf?version=1742821065Direct OA link when available
- Concepts
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Fire detection, Computer science, Algorithm, Engineering, Architectural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.on | 163, 217 |
| abstract_inverted_index.to | 77, 108, 138, 172, 196 |
| abstract_inverted_index.50% | 197 |
| abstract_inverted_index.and | 8, 60, 74, 118, 152, 191, 208 |
| abstract_inverted_index.for | 16, 69 |
| abstract_inverted_index.its | 230 |
| abstract_inverted_index.key | 141 |
| abstract_inverted_index.low | 9 |
| abstract_inverted_index.the | 2, 46, 52, 56, 64, 87, 120, 127, 149, 164, 173, 179, 185, 193, 199, 225 |
| abstract_inverted_index.1.7% | 210 |
| abstract_inverted_index.4.8% | 204 |
| abstract_inverted_index.This | 102, 146 |
| abstract_inverted_index.fire | 13, 26, 156, 167 |
| abstract_inverted_index.head | 129, 131 |
| abstract_inverted_index.high | 5 |
| abstract_inverted_index.load | 59, 181 |
| abstract_inverted_index.neck | 88 |
| abstract_inverted_index.size | 195 |
| abstract_inverted_index.this | 20 |
| abstract_inverted_index.ADown | 65 |
| abstract_inverted_index.model | 116, 175, 194 |
| abstract_inverted_index.novel | 32 |
| abstract_inverted_index.paper | 21 |
| abstract_inverted_index.range | 49 |
| abstract_inverted_index.robot | 222 |
| abstract_inverted_index.that, | 170 |
| abstract_inverted_index.under | 158 |
| abstract_inverted_index.using | 39, 94 |
| abstract_inverted_index.while | 81, 201 |
| abstract_inverted_index.47.2%, | 183 |
| abstract_inverted_index.52.2%, | 190 |
| abstract_inverted_index.First, | 30 |
| abstract_inverted_index.P-C2f, | 36 |
| abstract_inverted_index.better | 139 |
| abstract_inverted_index.count. | 62 |
| abstract_inverted_index.design | 76 |
| abstract_inverted_index.fusion | 90, 99, 121 |
| abstract_inverted_index.module | 53, 66, 100, 103 |
| abstract_inverted_index.number | 186 |
| abstract_inverted_index.tunnel | 18, 25, 166, 219 |
| abstract_inverted_index.(CCFF). | 101 |
| abstract_inverted_index.achieve | 109 |
| abstract_inverted_index.action, | 51 |
| abstract_inverted_index.address | 1 |
| abstract_inverted_index.capture | 140 |
| abstract_inverted_index.complex | 144 |
| abstract_inverted_index.current | 12 |
| abstract_inverted_index.dataset | 168 |
| abstract_inverted_index.dynamic | 128 |
| abstract_inverted_index.feature | 33, 84, 89, 112 |
| abstract_inverted_index.further | 114 |
| abstract_inverted_index.fusion, | 113 |
| abstract_inverted_index.highway | 17 |
| abstract_inverted_index.module, | 35 |
| abstract_inverted_index.network | 91 |
| abstract_inverted_index.partial | 40 |
| abstract_inverted_index.reduces | 55, 178, 192 |
| abstract_inverted_index.results | 162 |
| abstract_inverted_index.scenes. | 145 |
| abstract_inverted_index.targets | 157 |
| abstract_inverted_index.(PConv). | 42 |
| abstract_inverted_index.Finally, | 126 |
| abstract_inverted_index.YOLOv8s, | 176 |
| abstract_inverted_index.accuracy | 10, 151, 207 |
| abstract_inverted_index.baseline | 174 |
| abstract_inverted_index.compared | 171 |
| abstract_inverted_index.designed | 38 |
| abstract_inverted_index.enhances | 148 |
| abstract_inverted_index.increase | 211 |
| [email protected]. | 213 |
| abstract_inverted_index.minimize | 78 |
| abstract_inverted_index.multiple | 135 |
| abstract_inverted_index.platform | 223 |
| abstract_inverted_index.proposes | 22 |
| abstract_inverted_index.reducing | 115 |
| abstract_inverted_index.validate | 224 |
| abstract_inverted_index.CNN-based | 97 |
| abstract_inverted_index.Secondly, | 86 |
| abstract_inverted_index.achieving | 202 |
| abstract_inverted_index.branching | 75 |
| abstract_inverted_index.decreases | 184 |
| abstract_inverted_index.detecting | 155 |
| abstract_inverted_index.detection | 14, 27, 130 |
| abstract_inverted_index.efficient | 110 |
| abstract_inverted_index.emergency | 220 |
| abstract_inverted_index.employing | 71 |
| abstract_inverted_index.enhancing | 119 |
| abstract_inverted_index.essential | 83 |
| abstract_inverted_index.features. | 125 |
| abstract_inverted_index.leverages | 104 |
| abstract_inverted_index.model’s | 57, 150 |
| abstract_inverted_index.original, | 200 |
| abstract_inverted_index.parameter | 61 |
| abstract_inverted_index.practical | 227 |
| abstract_inverted_index.algorithm, | 28 |
| abstract_inverted_index.algorithms | 15 |
| abstract_inverted_index.challenges | 3 |
| abstract_inverted_index.complexity | 7, 117 |
| abstract_inverted_index.confirming | 229 |
| abstract_inverted_index.deployment | 215 |
| abstract_inverted_index.efficiency | 122 |
| abstract_inverted_index.extraction | 34 |
| abstract_inverted_index.introduced | 68 |
| abstract_inverted_index.kernel’s | 48 |
| abstract_inverted_index.mechanisms | 137 |
| abstract_inverted_index.operations | 107 |
| abstract_inverted_index.parameters | 188 |
| abstract_inverted_index.preserving | 82 |
| abstract_inverted_index.real-world | 233 |
| abstract_inverted_index.redesigned | 93 |
| abstract_inverted_index.robustness | 153 |
| abstract_inverted_index.scenarios, | 19 |
| abstract_inverted_index.scenarios. | 234 |
| abstract_inverted_index.algorithmic | 6 |
| abstract_inverted_index.challenging | 159 |
| abstract_inverted_index.conditions. | 160 |
| abstract_inverted_index.convolution | 41, 47, 106 |
| abstract_inverted_index.cross-scale | 98, 111 |
| abstract_inverted_index.demonstrate | 169 |
| abstract_inverted_index.determining | 45 |
| abstract_inverted_index.dynamically | 44 |
| abstract_inverted_index.experiments | 216 |
| abstract_inverted_index.improvement | 147, 205 |
| abstract_inverted_index.information | 142 |
| abstract_inverted_index.introduced, | 133 |
| abstract_inverted_index.lightweight | 24, 73, 96, 105 |
| abstract_inverted_index.multi-scale | 124 |
| abstract_inverted_index.Experimental | 161 |
| abstract_inverted_index.FIRE-YOLOv8s | 177 |
| abstract_inverted_index.Furthermore, | 214 |
| abstract_inverted_index.firefighting | 221 |
| abstract_inverted_index.information. | 85 |
| abstract_inverted_index.requirements | 80 |
| abstract_inverted_index.Additionally, | 63 |
| abstract_inverted_index.FIRE-YOLOv8s. | 29 |
| abstract_inverted_index.algorithm’s | 226 |
| abstract_inverted_index.computational | 58, 79, 180 |
| abstract_inverted_index.downsampling, | 70 |
| abstract_inverted_index.effectiveness | 231 |
| abstract_inverted_index.incorporating | 134 |
| abstract_inverted_index.significantly | 54 |
| abstract_inverted_index.applicability, | 228 |
| abstract_inverted_index.self-attention | 136 |
| abstract_inverted_index.self-constructed | 165 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.10607777 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |