Intelligent Firefighting Technology for Drone Swarms with Multi-Sensor Integrated Path Planning: YOLOv8 Algorithm-Driven Fire Source Identification and Precision Deployment Strategy
Abstract
1. Introduction
2. Literature Review
3. Research Methodology
3.1. Fire Source Identification Method
3.2. Multi-Sensor Data Fusion
Algorithm 1: IAO Algorithm Pseudo-Code |
1. Initialize the flight tasks and the positions of individuals . Set the maximum number of iterations () and the learning rate (). |
2. For each individual, gather information and update the position: |
3. Evaluate and filter the collected information. Adjust the position based on a random condition: |
, then |
4. Perform information analysis and organization, adjusting positions using |
, then |
5. Once the iteration is complete, output the optimal path and terminate the process. |
Algorithm 2: CFOA Algorithm Pseudo-Code |
1. Initialize the fish swarm position: Randomly distribute in the search space. Set the maximum number of iterations and the capture rate . |
2. Exploration phase: Update the capture rate using the formula: |
3. Fitness calculation: Compute the fitness of each individual and update the fish swarm position using Equation (12). |
4. Independent search phase: Perform the independent search phase using the formula in Equation (13). |
5. Group fishing phase: Calculate the centroid of the fish swarm , and update the positions. |
6. Development phase: Update the position of each individual: |
7. Termination: If the maximum number of iterations is reached, output the optimal path and terminate. |
3.3. Experimental Data
4. Results Analysis
4.1. Fire Source Detection Accuracy Evaluation
4.2. Path Planning Efficiency Evaluation
4.3. Fire Suppression Material Deployment Accuracy
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Fire Source Type | Criteria | YOLOv8 Accuracy | Optimized Accuracy | False Detection Rate Decrease |
---|---|---|---|---|
Small Fire Source | Flame area < 1 m2, Heat radiation < 50 kW/m2 82.3% | 82.3% | 94.6% | 12.7% |
Medium Fire Source | 1 m2 ≤ area < 5 m2, 50–200 kW/m2 | 88.1% | 96.2% | 9.5% |
Large Fire Source | area ≥ 5 m2, Heat radiation ≥ 200 kW/m2 | 91.4% | 97.8% | 5.2% |
Multi-fire Source | 3 independent fire points present simultaneously | 76.5% | 92.1% | 15.6% |
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Yu, B.; Yu, S.; Zhao, Y.; Wang, J.; Lai, R.; Lv, J.; Zhou, B. Intelligent Firefighting Technology for Drone Swarms with Multi-Sensor Integrated Path Planning: YOLOv8 Algorithm-Driven Fire Source Identification and Precision Deployment Strategy. Drones 2025, 9, 348. https://doi.org/10.3390/drones9050348
Yu B, Yu S, Zhao Y, Wang J, Lai R, Lv J, Zhou B. Intelligent Firefighting Technology for Drone Swarms with Multi-Sensor Integrated Path Planning: YOLOv8 Algorithm-Driven Fire Source Identification and Precision Deployment Strategy. Drones. 2025; 9(5):348. https://doi.org/10.3390/drones9050348
Chicago/Turabian StyleYu, Bingxin, Shengze Yu, Yuandi Zhao, Jin Wang, Ran Lai, Jisong Lv, and Botao Zhou. 2025. "Intelligent Firefighting Technology for Drone Swarms with Multi-Sensor Integrated Path Planning: YOLOv8 Algorithm-Driven Fire Source Identification and Precision Deployment Strategy" Drones 9, no. 5: 348. https://doi.org/10.3390/drones9050348
APA StyleYu, B., Yu, S., Zhao, Y., Wang, J., Lai, R., Lv, J., & Zhou, B. (2025). Intelligent Firefighting Technology for Drone Swarms with Multi-Sensor Integrated Path Planning: YOLOv8 Algorithm-Driven Fire Source Identification and Precision Deployment Strategy. Drones, 9(5), 348. https://doi.org/10.3390/drones9050348