Table 1.

Summary of existing literature on deep learning–based fire detection.

Ref.	Dataset used	AI model used	Features	Accuracy	Contribution	Limitation
10	Custom dataset with fire and non-fire images	Pre-trained CNNs (VGG16, InceptionV3, Xception) with LwF	Automatic CNN features	98.72% (Xception)	LwF maintains performance on original and new tasks	Limited to image-based detection
9	Fire and smoke images from diverse environments	Two-stage Faster R-CNN with hybrid features	Static + dynamic	96.5% (accuracy)	Hybrid feature extraction for improved detection	High computational complexity
15	Utah Desert Fire; DeepFire	Modified ResNet-50; Xception	Visual features	100% (Utah Desert); 99.22% (DeepFire)	DL methods tailored for desert/forest fire detection	Lacks attention mechanisms to focus fire regions
11	Infrared and visible wildfire images	FIRe-GAN (fusion)	IR–visible fused features	Not specified	Enhanced detection via cross-modal fusion	No quantitative accuracy metrics
12	M3FD dataset	IA-VFDnet (CNN– Transformer hybrid)	Multimodal (IR–visible)	Superior to SOTA	Hybrid learning for high -quality fusion detection	Edge-device feasibility not evaluated
13	Landsat-8 imagery	Convolutional Neural Networks (CNNs)	Spectral bands	Precision: 87.2%, Recall: 92.4%	Satellite-based DL detection with good PR	Performance reliant on image quality
14	USTC_SmokeRS; Landsat_Smk	CNNs with Input Amplification (InAmp)	Spectral patterns	Improved over baseline	Enhanced class-specific spectral pattern learning	Limited cross-dataset generalization