Work

Real-time CCTV-based deep learning for early detection of lithium-ion battery fires

Year: 2025

Type: article

Abstract: Lithium-ion battery (LIB) fires pose distinctive hazards—including toxic gas release, resistance to suppression, and potential re-ignition—making their early detection critical for effective mitigation. Current LIB fire detection approaches often rely on embedded sensors or wiring, limiting applicability in portable devices such as laptops, and e-scooters. Moreover, conventional fire detection systems cannot distinguish between LIB and non-LIB fires, limiting situational awareness and delaying appropriate response. This study presents a real-time deep learning framework for LIB fire recognition using CCTV footage, leveraging characteristic spatio-temporal combustion patterns such as jet-like flame projection, abrupt ignition bursts, temporary flame extinction, and re-ignition. A 3D convolutional neural network with a ResNet-based backbone was trained on a custom dataset comprising LIB fires, conventional fires, and non-fire scenes from diverse real-world environments. The model achieved ∼87 % accuracy, with balanced precision, recall, and F1-score, and processed video at 94 FPS with low false-alarm and miss rates. Temporal prediction analysis revealed that short-term classification fluctuations corresponded with actual combustion stages, providing interpretable insights into fire progression. Grad-CAM++ visualizations confirmed the network's focus on relevant LIB fire features. The proposed framework combines high accuracy, interpretability, and operational feasibility, offering deployment potential across residential, industrial, and transportation environments. Its adoption will enable early intervention, improve decision-making, and support safer integration of LIB-powered technologies in everyday life. • AI model detects fire and classifies LIB vs. conventional vs. non-fire. • 3D CNN model trained on diverse real-world electrified fire scenarios. • Achieved 87 % accuracy with balanced precision, recall, and F1-score. • Heatmaps showed AI focused on key visual cues in LIB and conventional fires. • Supports early, informed response and safer electrified environments.

Source: Journal of Power Sources

DOI: 10.1016/j.jpowsour.2025.238452

Authors: Mostafa M.E. H. Ali, Murat Tahtali, Maryam Ghodrat

Institution: UNSW Canberra

Language: English

Topic: Fire Detection and Safety Systems

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $4,230