Real-Time Fire Escape Path Guidance System through YOLO11 and Dynamic A* Algorithm

We present a closed-loop fire escape guidance system that integrates YOLO11 flame detection, map-aligned hazard mapping, and event-triggered A* replanning. Corridor-camera detections are converted into grid cells, expanded by a 3 × 3 safety buffer, and updated by a time-dependent four-neighbor fire-spread model. A* recomputes routes to the nearest stairwell when a new flame appears, fire spreads, a restored cell reopens, or the start position changes. The system displays the live hazard map and route and transmits alarms through the ServerChan Turbo API to WeChat (version 3.9.10.19, Tencent, Shenzhen, China). Each alarm contains the detection time, start position, path length, and flame count. On the fixed validation split, the deployment YOLO11m checkpoint achieves 77.34% [email protected]; on a duplicate-aware group-disjoint test set, the final model achieves 72.36%. A single A* call averages 0.407 ± 0.011 ms on an Intel Core i5-14600KF CPU (Intel Corporation, Santa Clara, CA, USA). In a controlled 60 s scenario with 3 s updates, the system finds a fire-free, buffer-clear route at 30.0% of replanning events, and the common-timeline evacuation run reaches the stair region in 15.83 s. Grid maps from 21 × 21 to 81 × 81 are processed successfully.

Authors

Institutions

Publication Details

Journal
AI Engineering
Published
2026-11-11
DOI
https://doi.org/10.53941/aieng.2026.100022
Primary Topic
Evacuation and Crowd Dynamics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Real-Time Fire Escape Path Guidance System through YOLO11 and Dynamic A* Algorithm

Tianyu Shi, Lincong Zhang, Qiang Fu, Jun Ang et al.
AI Engineering
Evacuation and Crowd Dynamics
article

Real-Time Fire Escape Path Guidance System through YOLO11 and Dynamic A* Algorithm

Tianyu Shi, Lincong Zhang, Qiang Fu, Jun Ang, Xiangyu Liu, Lei Guo
article en

Abstract

We present a closed-loop fire escape guidance system that integrates YOLO11 flame detection, map-aligned hazard mapping, and event-triggered A* replanning. Corridor-camera detections are converted into grid cells, expanded by a 3 × 3 safety buffer, and updated by a time-dependent four-neighbor fire-spread model. A* recomputes routes to the nearest stairwell when a new flame appears, fire spreads, a restored cell reopens, or the start position changes. The system displays the live hazard map and route and transmits alarms through the ServerChan Turbo API to WeChat (version 3.9.10.19, Tencent, Shenzhen, China). Each alarm contains the detection time, start position, path length, and flame count. On the fixed validation split, the deployment YOLO11m checkpoint achieves 77.34% [email protected]; on a duplicate-aware group-disjoint test set, the final model achieves 72.36%. A single A* call averages 0.407 ± 0.011 ms on an Intel Core i5-14600KF CPU (Intel Corporation, Santa Clara, CA, USA). In a controlled 60 s scenario with 3 s updates, the system finds a fire-free, buffer-clear route at 30.0% of replanning events, and the common-timeline evacuation run reaches the stair region in 15.83 s. Grid maps from 21 × 21 to 81 × 81 are processed successfully.

AI EngineeringVol. 2(2)
Shenyang Ligong University (CN), NARI Group (China) (CN), Northeastern University (CN)
Evacuation and Crowd Dynamics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon