Open research questions in Fire Detection and Safety Systems
78 gap statements mined from Fire Detection and Safety Systems papers in our 4.5M-paper local library, which holds 489 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.
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What the literature leaves open
The lack of effective fire detection and navigation systems in hazardous environments. The limitations of older fire fighting systems, which were slow to respond and prone to false alarms.
The fuzzy logic inference system uses fixed distance and velocity ranges (3-1008 cm for distance, 10-50 cm/s for velocity); optimization of these parameters for different fire environments and scaling to larger operational areas is not addressed.
The current wardrobe has technical problems such as low efficiency of extracting clothes and lack of intelligent monitoring. The lack of intelligent monitoring can lead to problems such as mycosis and mildew in clothes.
One of the challenges encountered during the implementation was ensuring the reliability of the sensors in detecting fires and environmental changes. The sensitivity of the sensors needed to be adjusted to prevent false alarms while also ensuring that fires were detected quickly and accurately.
Design and Development of a Smart F ire Fighting Robot for Hazardous Environment Applications · 2026 · DOIThe existing surveillance systems are primarily reactive and rely on continuous human monitoring. The existing systems often fail to capture complex relationships between people in a scene. The proposed system addresses these limitations.
The increasing incidence of firearm-related violence in public spaces has created an urgent need for intelligent surveillance systems capable of real-time detection and monitoring. Traditional surveillance systems rely heavily on human monitoring, which is often inefficient, error-prone, and unable to respond quickly in critical situations.
The need for an intelligent and integrated approach to fire safety. The lack of cost-effective and scalable fire detection systems. The limitation of traditional fire detection systems in providing real-time response and emergency guidance.
FireGuard AI: An Intelligent Real-Time Fire Detection and Emergency Response System Using Computer Vision and Artificial Intelligence · 2026 · DOIThere is a lack of scholarly attention to forest fires in Turkey despite their significance. Previous studies have limitations in terms of centralized processing or standalone algorithms. There is a need for a novel hybrid model that combines the strengths of different algorithms for wildfire detection and prediction.
An intelligent IoT–machine learning framework for wildfire detection and prediction using a hybrid RF–XGB model · 2026 · DOIThe system's performance is evaluated through a limited number of controlled trials.
Our future work will consider incorporating additional sensors, including photoelectric smoke detectors, flame sensors, particulate matter monitors to improve detection accuracy across diverse fire‑precursor signatures, integrate a GSM module as a fallback for mobile notifications through SMS when Wi-Fi is unavailable.
Design and implementation of an IoT-based fire detection and alert system for real-time hazard monitoring · 2026 · DOIFuture work should investigate more efficient message formats, image transport methods, and optimized DDS configurations. Future research will focus on connecting ROS 2 data streams from the UAV to an Unreal Engine–based visualization environment to enable real-time three-dimensional scene reconstruction, thermal hotspot visualization, and improved situational awareness for incident commanders.
Future work will focus on further deployment and optimization on the NVIDIA Jetson Nano platform, with particular attention paid to real-time performance under practical constraints such as limited power supply, varying environmental conditions, and dynamic flight scenarios. However, the present deployment validation remains limited to a brief embedded test.
ASCA-YOLO: Adaptive Sparse and Context-Aware YOLO Algorithm for Forest Wildfire Detection · 2026 · DOIThe need for a smart fire extinguisher switch mechanism with an integrated safety monitoring system. The lack of a globally recognizable visual architecture in traditional fire extinguishing technology.
The intersection of spectral pattern learning and hardware-efficient deployment has not been studied. (2024) represents a promising approach to embedding domain knowledge into learnable preprocessing, but its computational overhead on different hardware backends — particularly MPS versus CUDA — has not been characterised.
A COMPREHENSIVE REVIEW OF DEEP LEARNING-BASED FOREST FIRE AND SMOKE DETECTION: MODEL ARCHITECTURES, HARDWARE BACKENDS, AND CROSS-PLATFORM BENCHMARKING · 2026 · DOILimited functionality of traditional kitchen safety devices. Lack of integration with modern communication networks in traditional kitchen safety systems.
Machine Learning and AI systems can be implemented to improve the performance of the robot.
Design and Development of a Smart F ire Fighting Robot for Hazardous Environment Applications · 2026 · DOIThe complexity of urban scenes limits the effectiveness of current fire detection methods.
Real-Time Early Warning of Incipient Fire in Multiple Urban Scenarios: A Deep Learning-Based Monitoring Method · 2026 · DOISeveral directions are identified for future development. Domain-Specific Model Fine-Tuning: Training YOLOv8 or YOLO11 variants on annotated disasterscene datasets including fire, smoke, rubble, and collapsed structures would replace the HSV and edge density modules with more precise neural alternatives.
Multimodal Computer Vision for Rapid Disaster Damage Analysis and Victim Detection using Deep Learning · 2026 · DOITraditional kitchens rely on manual monitoring of appliances and gas usage, which is prone to human error and delayed emergency responses. Traditional kitchen safety devices provide essential alerts but often fail to intervene during emergencies or inform users remotely.
Place units in hallways, near bedrooms, and close to gas appliances. For LPG (heavier than air), mount 6–12 inches above the floor; for methane (lighter than air), mount near the ceiling. Keep at least 3 meters from cooking surfaces to reduce false alarms. Test monthly. Press and hold the test button for three seconds each month to verify buzzer, indicator lights, and emergency LEDs. This takes less than 30 seconds.
Design And Development of Dual Source Emergency Light with Integrated Smoke and Gas Detection · 2026 · DOIImproving the accuracy and robustness of the fire detection model. Integrating FireGuard AI with other safety systems and devices. Deploying FireGuard AI in various environments and evaluating its performance.
FireGuard AI: An Intelligent Real-Time Fire Detection and Emergency Response System Using Computer Vision and Artificial Intelligence · 2026 · DOIFuture work will focus on the following aspects: (1) Integrate dynamic event-triggered mechanisms and sensor fault-tolerant control strategies to improve stability in complex warehouse environments; (2) upgrade the hardware platform to enhance load capacity and motion stability; (3) introduce 5G communication to realize remote monitoring and cloud management; and (4) optimize the fire detection algorithm and expand…
A Multi-Controller Embedded Intelligent Crane System with Integrated Fire Safety for Light-Load Material Handling · 2026 · DOIThe integration of AI and Deep Learning to enhance the system's understanding of its environment. The development of more advanced sensor-driven navigation and alert transmission systems.
Extending the model architecture to account for tunnel gradient and curvature. Employing more advanced architectures like Graph Neural Networks (GNNs) to generalize across non-Euclidean spatial structures.
Spatio-Temporal Prediction of Critical Smoke Properties in Tunnel Fires Using a Hybrid CNN-LSTM Network · 2026 · DOITesting the Fireguard Approach in different environments and conditions. Improving the autonomous flights of UAS without backup of a drone-pilot. Developing new applications for the use of 5G mobile networks in Wildfire Management.
Fireguard: A Real-Time Wildfire Monitoring and Risk Assessment System Using Unmanned Aerial Systems and Multi-Sensor Fusion · 2026 · DOI
Most-cited papers in Fire Detection and Safety Systems
- A Forest Fire Detection System Based on Ensemble Learning · Forests · 2021 · 494 citations
- Aerial imagery pile burn detection using deep learning: The FLAME dataset · Computer Networks · 2021 · 360 citations
- Attention based CNN model for fire detection and localization in real-world images · Expert Systems with Applications · 2021 · 244 citations
- Forest fire and smoke detection using deep learning-based learning without forgetting · Fire Ecology · 2023 · 230 citations
- Fire Sensing Technologies: A Review · IEEE Sensors Journal · 2019 · 214 citations
- Forest fire detection system using wireless sensor networks and machine learning · Scientific Reports · 2022 · 211 citations
- Active fire detection in Landsat-8 imagery: A large-scale dataset and a deep-learning study · ISPRS Journal of Photogrammetry and Remote Sensing · 2021 · 167 citations
- An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach · Sensors · 2023 · 140 citations
- A modified YOLOv5 architecture for efficient fire detection in smart cities · Expert Systems with Applications · 2023 · 136 citations
- Assessing the Effectiveness of YOLO Architectures for Smoke and Wildfire Detection · IEEE Access · 2023 · 133 citations
Most recent work
- A Lightweight Deep and Wide Network for Image-Based Detection of Industrial Waste Gas · IEEE Transactions on Circuits and Systems for Video Technology · 2026
- EF-YOLO: Detecting Small Targets in Early-Stage Agricultural Fires via UAV-Based Remote Sensing · Remote Sensing · 2026
- A digital twin for diesel engines: Operator-infused physics-informed neural networks with transfer learning for engine health monitoring · Engineering Applications of Artificial Intelligence · 2026
- Ferroelectric Optoelectronic Sensor for Intelligent Flame Detection and In-Sensor Motion Perception · Nano-Micro Letters · 2026
- Early fire detection using embedded and deep learning technologies to improve sustainable agriculture and food security · Knowledge-Based Systems · 2026
- Real-time safety helmet detection using multi-scale feature-gated fusion · Automation in Construction · 2026
- Advanced evaluation of pre-trained CNN models for accurate forest fire detection · Natural Hazards · 2026
- Cross-modal distillation for real-time wildfire detection and localization in edge-deployed aerial vehicles · ISPRS Journal of Photogrammetry and Remote Sensing · 2026
- FFDNet: An end-to-end lightweight real-time fire detection model for forest fire environments · Physics and Chemistry of the Earth, Parts A/B/C · 2026
- YOLOFlame: Benchmarking vehicle fire and smoke detection in urban environments · Alexandria Engineering Journal · 2026
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