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Fast fire flame detection in surveillance video using logistic regression and temporal smoothing

Fire detection technologies remain a critical component of building automation. With the recent significant advances in computer vision, visual fire detection methods have been developed and integrated into building surveillance systems. Overfitting and accuracy challenges remain in fire detection when training datasets are limited. In this work, we tackle these challenges by developing a deep convolutional generative adversarial network (DCGAN) for highly accurate visual fire detection when training images are limited. Our model addresses three types of errors in visual fire detection with small training datasets: model overfitting, fire probability overestimation, and fire probability underestimation. The DCGAN includes a generator of fake fire images for self-supervised learning (SSL) and a discriminator for image classification. We designed computational experiments with high-quality datasets to test and validate our model against other supervised learning approaches. We also benchmarked the performance of the DCGAN against a best-in-class deep visual fire detection model. The results show that our model significantly outperforms other fire detection models on all performance metrics when trained with the same small dataset. The results demonstrate that the DCGAN effectively mitigates the three types of error when the training dataset is limited.

Fire detection from remote sensing images plays a critical role in early fire warning, fire risk reduction, and post-fire damage assessment. Though deep learning-based approaches have achieved huge successes in fire detection and locating, their data-driven nature requires substantial human resources and time for meticulously labeling pixel-level annotations. The challenge of limited well-annotated labels is still hampering its application. In this paper, we propose a novel label evolution method, Box2Pix, that is to automatically transform box-level labels into pixel-levels labels, alleviating the heavy labor and time cost of pixel-wise fire annotation. Box2Pix leverages box-level annotated object detection datasets with the pre-trained large segmentation models to generate pixel-level annotated semantic segmentation datasets to further boost fire detection performance. Moreover, we introduce innovative point prompt generation methods by leveraging superpixel segmentation and saliency detection to identify potential target regions for generating random points, augmenting the semantic segmentation capability of Segment Anything Model (SAM). Box2Pix eliminates the need for extensive human involvement, avoids reliance on large-scale datasets for training or fine-tuning, enhancing the speed and quality of pixel-level label generation while reducing annotation difficulty and costs. The experiment on the highly generalized Flame And Smoke Detection Dataset (FASDD) demonstrates its superiority over the existing state-of-the-art methods, with improvements of 52.51% in mIoU and 41.38% in overall accuracy. Based on Box2Pix, the largest pixel-level fire detection dataset, Flame And Smoke Segmentation Dataset (FASSD) is generated. The newly created dataset is accessible on: https://github.com/OyamingO/Box2Pix.

Fire detection is a critical safety issue due to the major and irreversible consequences of fire, from economic prejudices to loss of life. It is therefore of utmost importance to design reliable, automated systems that can issue early alarms. The objective of this review is to present the state of the art in the area of fire detection, prevention and propagation modeling with machine learning algorithms. In order to understand how an artificial intelligence application penetrates an area of fire detection, a quantitative scientometric analysis was first performed. A literature search process was conducted on the SCOPUS database using terms and Boolean expressions related to fire detection techniques and machine learning areas. A number of 2332 documents were returned upon the bibliometric analysis. Fourteen datasets used in the training of deep learning models were examined, discussing critically the quality parameters, such as the dataset volume, class imbalance, and sample diversity. A separate discussion was dedicated to identifying issues that require further research in order to provide further insights, and faster and more accurate models.. The literature survey identified the main issues the current research should address: class imbalance in datasets, misclassification, and datasets currently used in model training. Recent advances in deep learning models such as transfer learning and (vision) transformers were discussed.

Forest fire (wildfire) is one of the common hazards that is accrued in the forest. Fire monitoring has three phases: pre-fire (take appropriate action for fire control), during fire (detection of fire and planning to control fire), post-fire (damage assessment and mitigation planning). In older days, manually fire detection approach is used. In current days, satellite-based surveillance system is used to detect forest fire but this works when fire is spread in the large area. So these techniques are not efficient. According to a survey, approximately 80% losses are accrued in the forest due to the late detection of fire. So to overcome this problem, we use the Internet of things technology. In this paper, early fire detection model has been proposed with the help of the Raspberry Pi microcontroller and required sensors. Centralized server is used for storing the data and analyzing that data. Feed-forward fully connected neural network is used for prediction purpose. Then, an alert message is sent to the admin and to the people within the proximity.

Fire detection is a critical task in ensuring the safety of human lives and property. In recent years, deep learning techniques, such as Convolutional Neural Networks (CNNs), have shown promising results in fire detection due to their ability to automatically learn relevant features from images. In this project, we propose a real-time fire detection system using CNNs to detect fires in images or video streams. The proposed system consists of a CNN-based architecture that includes multiple convolutional layers for feature extraction, activation functions for introducing non-linearity, pooling layers for spatial dimension reduction, and fully connected layers for global pattern learning. The architecture is trained on a labelled dataset of fire and non-fire images or videos using optimization algorithms to minimize the loss function. To achieve real-time fire detection, the trained CNN model is integrated into a fire detection system that can process images or video streams in real-time. The system captures images or video frames from a live feed, pre-processes the data, and passes it through the CNN model for fire detection. The system can generate alerts or notifications in real-time when fire is detected, allowing for prompt response and mitigation measures. The performance of the proposed fire detection system is evaluated using metrics such as accuracy, precision, recall, and F1-score. The system is tested on different datasets and under various conditions to assess its accuracy, robustness, and real-time processing capabilities. The results of this project demonstrate the effectiveness of CNNs for real-time fire detection and highlight the potential of the proposed system for enhancing fire safety in various applications, including surveillance systems, smart buildings, and industrial settings. The developed system can be used as an early warning tool for fire detection, enabling timely response and mitigation, and helping to prevent or minimize the damage caused by fires. Keywords: Fire

A modified YOLOv5 architecture for efficient fire detection in smart cities

CNN-based fire detection method on autonomous ships using composite channels composed of RGB and IR data

Forest fires are severe ecological disasters worldwide that cause extensive ecological destruction and economic losses while threatening biodiversity and human safety. With the escalation of climate change, the frequency and intensity of forest fires are increasing annually, underscoring the urgent need for effective monitoring and early warning systems. This study investigates the application effectiveness of deep learning-based object detection technology in forest fire smoke recognition by using the YOLOv11x algorithm to develop an efficient fire detection model. The objective is to enhance early fire detection capabilities and mitigate potential damage. To improve the model’s applicability and generalizability, two publicly available fire image datasets, WD (Wildfire Dataset) and FFS (Forest Fire Smoke), encompassing various complex scenarios and external conditions, were employed. After 501 training epochs, the model’s detection performance was comprehensively evaluated via multiple metrics, including precision, recall, and mean average precision (mAP50 and mAP50-95). The results demonstrate that YOLOv11x excels in bounding box loss (box loss), classification loss (cls loss), and distribution focal loss (dfl loss), indicating effective optimization of object detection performance across multiple dimensions. Specifically, the model achieved a precision of 0.949, a recall of 0.850, an mAP50 of 0.901, and an mAP50-95 of 0.786, highlighting its high detection accuracy and stability. Analysis of the precision‒recall (PR) curve revealed an average mAP@0.5 of 0.901, further confirming the effectiveness of YOLOv11x in fire smoke detection. Notably, the mAP@0.5 for the smoke category reached 0.962, whereas for the flame category, it was 0.841, indicating superior performance in smoke detection compared with flame detection. This disparity primarily arises from the distinct visual characteristics of flames and smoke; flames possess more vivid colors and defined shapes, facilitating easier recognition by the model, whereas smoke exhibits more ambiguous and variable textures and shapes, increasing detection difficulty. In the test set, 86.89% of the samples had confidence scores exceeding 0.85, further validating the model’s reliability. In summary, the YOLOv11x algorithm demonstrates excellent performance and broad application potential in forest fire smoke recognition, providing robust technical support for early fire warning systems and offering valuable insights for the design of intelligent monitoring systems in related fields.

Current indoor fire detection systems often have limitations, including insufficient intelligence and slow response times. Recent studies have started to explore AI vision-based methods for real-time fire and smoke detection to replace traditional fire sensors. However, indoor environments present many interference factors, and few methods can effectively demonstrate superior fire and smoke detection performance under multiple simulated interference conditions. To meet the requirements of real-time, high accuracy and anti-interference, a 3D convolutional neural network (3D-CNN)-based real-time fire and smoke detection model has been developed via red-green-blue (RGB) and near-infrared (NIR) feature fusion. RGB and NIR images with complementary information were used as dual-stream inputs to the model. The feature extraction module was enhanced by 3D attention mechanisms to simultaneously capture dynamic features between frames and static features within frames of fire and smoke. A non-linear feature gated fusion module has been developed to solve the challenges of missing multi-modal features and insufficient fusion. To validate the engineering effectiveness of the model, a large-scale multi-modal dataset was constructed in real indoor building scenarios, and a real-time indoor fire and smoke detection system was also developed and deployed at the experimental site.

BDM-YOLOv8n: A high-performance model for accurate fire detection in aerial imagery.

Gas-to-Liquids (GTL) processing facilities face critical fire safety challenges due to the high flammability of gases, elevated temperatures, and complex industrial processes. Traditional fire detection systems—such as smoke, heat, and optical detectors—often underperform in such environments because of delayed response times, false alarms, and interference from dust, humidity, or vapors. To overcome these limitations, advanced technologies like artificial intelligence (AI), Internet of Things (IoT), and multispectral imaging are being integrated to enable real-time, accurate fire detection and prevention. AI-based systems, equipped with deep learning algorithms and predictive analytics, can process multispectral data and thermal imaging to detect early ignition signs with high precision. Simultaneously, IoT-enabled sensors create a continuous data network, allowing 24/7 surveillance and automated response. Computer vision and edge computing further enhance detection in obstructed or confined environments by analyzing complex thermal patterns in real time. Implementation strategies include sensor fusion, system optimization, and adaptive model training to ensure accuracy and minimize false positives. Real-world case studies illustrate the effectiveness of these technologies in mitigating risks and ensuring operational safety in GTL plants. The integration of AI and IoT marks a significant advancement in industrial fire prevention, enabling a proactive and resilient approach to safety in the energy sector.

This paper introduces a novel fire detection system that uses Convolutional Neural Networks (CNNs) to address the inefficiencies of traditional smoke and heat sensors, which are often slow, costly, and limited in their detection capabilities. The proposed approach processes a custom dataset containing video frames from CCTV cameras to train a machine-learning model for fire detection. With extensive preprocessing to eliminate noise and irrelevant data, the system has a high detection accuracy of about 93%, hence a promising alternative for fire detection in many environments. The study highlights the need for timely fire detection, especially in scenarios such as forests where fires cause significant environmental damage and contribute to climate-related issues. The system demonstrates adaptability and scalability, capable of being implemented in homes, industries, and forests. It concludes that integrating this CNN-based framework with wireless sensors and CCTV networks could enhance detection accuracy and efficiency, potentially reducing fire-related losses and enabling quicker responses to emergencies. Keywords: Fire detection, Convolutional neural networks, Machine learning, CCTV, Object detection.

Wildfires have a significant impact on the atmosphere, terrestrial ecosystems, and society. Real-time monitoring of wildfire locations is crucial in fighting wildfires and reducing human casualties and property damage. Geostationary satellites offer the advantage of high temporal resolution and are gradually being used for real-time fire detection. In this study, we constructed a fire label dataset using the stable VNP14IMG fire product and used the random forest (RF) model for fire detection based on Himawari-8 multiband data. The band calculation features related brightness temperature, spatial features, and auxiliary data as input used in this framework for model training. We also used a recursive feature elimination method to evaluate the impact of these features on model accuracy and to exclude redundant features. The daytime and nighttime RF models (RF-D/RF-N) are separately constructed to analyze their applicability. Finally, we extensively evaluated the model performance by comparing them with the Japan Aerospace Exploration Agency (JAXA) wildfire product. The RF models exhibited higher accuracy, with recall and precision rates of 95.62% and 59%, respectively, and the recall rate for small fires was 19.44% higher than that of the JAXA wildfire product. Adding band calculation features and spatial features, as well as feature selection, effectively reduced the overfitting and improved the model’s generalization ability. The RF-D model had higher fire detection accuracy than the RF-N model. Omission errors and commission errors were mainly concentrated in the adjacent pixels of the fire clusters. In conclusion, our VIIRS fire product and Himawari-8 data-based fire detection model can monitor the fire location in real time and has excellent detection capability for small fires, making it highly significant for fire detection.

With the growing frequency of fire incidents, the demand for rapid and accurate fire detection technologies has become increasingly critical. In this study, we evaluate segmentation-based object detection models YOLO (You Only Look Once) v5-seg, YOLOv8-seg, and YOLOv11-seg for their ability to detect flames and smoke under identical experimental conditions. A total of 5,000 fire images were collected and split into training, validation, and test datasets. The same hardware environment and hyperparameter settings were used for model training to ensure a fair comparison. The experimental results reveal that YOLOv11-seg achieved the best overall performance, with a Precision of 0.710, Recall of 0.570, F1-score of 0.632, and mAP (mean Average Precision) 50 of 0.600. Notably, YOLOv11-seg achieved the highest Recall and mAP values for smoke detection, underscoring its effectiveness in identifying smoke—a critical factor for early fire detection. In terms of efficiency, YOLOv8-seg demonstrated the fastest inference speed, while YOLOv5-seg offered advantages in lightweight model size. However, YOLOv11-seg provided a balanced trade-off between computational cost and detection accuracy, making it the most suitable model for real-world fire response scenarios. Accordingly, this study proposes YOLOv11-seg as a robust baseline model for segmentation-based fire detection and provides a foundational reference for future research on deep learning-driven intelligent fire video analysis.