What question did this study set out to answer?

This research aims to evaluate YOLOv11 models for detecting electric vehicle fires and smoke emissions using different labeling methods.

February 8, 2026International Journal of Fire Science and Engineering0 citations

A Study on Performance Analysis of Electric Vehicle Fire and Smoke Object Detection Based on Different Labeling Methods Using the YOLOv11 Model

JCJiwon ChoiInha University EYEunji YoonSemyung University HJHaiyoung JungSemyung University

Key Points

This research aims to evaluate YOLOv11 models for detecting electric vehicle fires and smoke emissions using different labeling methods.
Conducted experiments on YOLOv11 variants (n, s, m, l, x)
Trained models on a dataset of 3,000 images with consistent hardware and hyperparameters
Evaluated models based on precision, recall, mAP, F1-score, and inference speed
Bounding-box models showed faster inference speed, with YOLOv11n achieving 44.62 FPS
YOLOv11m with segmentation reached a mAP50 of 0.7952 and F1-score of 0.7510
Segmentation proved more effective for smoke detection, capturing irregular boundaries better than bounding-box models

Abstract

This study evaluated the performance of YOLOv11 models trained to detect electric vehicle (EV) fires and smoke emission using bounding-box and instance segmentation annotations. Comprehensive experiments were conducted across all YOLOv11 variants (n, s, m, l, x) to analyze the trade-offs between speed and detection accuracy. The models were trained on a dataset consisting of 3,000 images depicting EV fires and smoke, which were annotated using Roboflow’s polygon tool under identical training conditions with consistent hardware and hyperparameters. The evaluation was based on precision, recall, and mean average precision at an intersection over union threshold of 0.50 (mAP50), as well as mAP50-95, F1-score, and inference speed in frames per second. The experimental results showed distinct advantages for each annotation method. Bounding-box models demonstrated superior inference speed, with the YOLOv11n model achieving the highest speed at 44.62 FPS. This makes these methods optimal for real-time EV fire monitoring applications. By contrast, segmentation models showed significantly higher accuracy in detecting objects with irregular boundaries, particularly smoke plumes. The YOLOv11m model with segmentation achieved optimal performance with a mAP50 of 0.7952 and an F1-score of 0.7510. The segmentation approach proved particularly effective for smoke detection, where bounding-box annotation frequently failed to accurately capture the irregular boundaries of smoke in an image, which resulted in detection failures for these critical fire indicators. These findings demonstrate that segmentation labeling is more effective in achieving accurate early detection and precise localization of EV fires and smoke emissions, despite the computational trade-off. The superior boundary definition capability of the segmentation approach is crucial for reliable smoke detection, which provides an important early warning of thermal runaway events in EV batteries. Future studies will focus on expanding the diversity of the dataset used to train the models by applying generative AI techniques such as GAN and diffusion models to further enhance detection robustness across varied fire scenarios.

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