Hybrid Reinforcement Learning-Based Hyper-Parameter Optimization with Yolov8 Indoor Fire Recognition

In recent years the complexity and cost of Fire and Gas detection systems have increased rapidly with no measurable reduction in fatality rates or other losses. A principal reason for this disappointing outcome despite substantial investment is that systems designs have lost the focus of what they are trying to achieve. Another reason worth exploring is that investment in other areas may result in greater benefits. For instance, diverting funds to preventing lifting accidents, helicopter failures and materials handling incidents, could result in greater OVERALL benefit since these account for a larger proportion of risks. Nevertheless, Fire and Gas detections systems have a major role to play in preventing escalation, and this paper shows how they can be made more efficient in both cost and performance by combining four methods: targeting perceived hazards,integration from concept stage with other safety designs,control of design with effective performance specification, andcontrol of modifications throughout the system's life.

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.

Fires constitute a significant risk to public safety and property, making early and accurate detection essential for an effective response and damage mitigation. Traditional fire detection methods have limitations in terms of accuracy and adaptability, particularly in complex environments in which various fire stages (such as smoke and active flames) need to be distinguished. This study addresses the critical need for a comprehensive fire detection system capable of multistage classification, differentiating between non-fire, smoke, apartment fires, and forest fires. We propose a deep learning-based model using a customized DenseNet201 architecture that integrates various preprocessing steps and explainable AI techniques, such as Grad-CAM++ and SmoothGrad, to enhance transparency and interpretability. Our model was trained and tested on a diverse, multisource dataset, achieving an accuracy of 97%, along with high precision and recall. The comparative results demonstrate the superiority of the proposed model over other baseline models for handling multistage fire detection. This research provides a significant advancement toward more reliable, interpretable, and effective fire detection systems capable of adapting to different environments and fire types, opening new possibilities for environmentally friendly fire type detection, ultimately enhancing public safety and enabling faster, targeted emergency responses.

The fire detection system combines the simultaneous measurements of smoke, carbon monoxide, and carbon dioxide. The security of campus against intruders moving in laboratories, class rooms, staffrooms or washrooms. The fire alarm system consists of Fire detectors (with can be smoke detector, heat, or Infra-Red detectors), control unit and alarm system. A fire detection system is developed based on the simultaneous measurements of temperature and smoke. The fire detection system with the alarm algorithm detected fires that were not alarmed by smoke sensors, and alarmed in shorter times than smoke sensors operating alone. Previous fire detection algorithms used data from sensors for temperature, smoke, and combustion products. The smoke sensor alarms when the analog output signal exceeds or equal the threshold value. The node includes analog sensors to measure smoke, carbon monoxide (CO) and temperature. A fire alarm system should reliably and in a timely way notify building occupants about the presence of fire indicators, such as smoke or high temperatures.

DEVELOPMENT OF COMPUTER VISION-BASED AUTOMATIC FLAME DETECTION ALGORITHM USING MATLAB SOFTWARE ENVIRONMENT

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

The paper starts with the introduction of the basic requirements of classical intrusion detection and alarm systems and fire detection systems. The drawbacks of such conventional systems are highlighted. Techniques of computer vision are employed to remove the drawbacks and at the same time increase the reliability and response rate of the systems. For security and low level fire detection, a fuzzy logic based image comparison algorithm is deemed adequate. In order to confirm the existence of fire or smoke, techniques related to optical flow are employed as high level fire or smoke detection, which generate a velocity field for the image so that the decision can be judged by using fizzy logic. Details of implementation and some experimental results have been included in the paper for illustration.

Authorities and policymakers in Korea have recently prioritized improving fire prevention and emergency response. Governments seek to enhance community safety for residents by constructing automated fire detection and identification systems. This study examined the efficacy of YOLOv6, a system for object identification running on an NVIDIA GPU platform, to identify fire-related items. Using metrics such as object identification speed, accuracy research, and time-sensitive real-world applications, we analyzed the influence of YOLOv6 on fire detection and identification efforts in Korea. We conducted trials using a fire dataset comprising 4000 photos collected through Google, YouTube, and other resources to evaluate the viability of YOLOv6 in fire recognition and detection tasks. According to the findings, YOLOv6’s object identification performance was 0.98, with a typical recall of 0.96 and a precision of 0.83. The system achieved an MAE of 0.302%. These findings suggest that YOLOv6 is an effective technique for detecting and identifying fire-related items in photos in Korea. Multi-class object recognition using random forests, k-nearest neighbors, support vector, logistic regression, naive Bayes, and XGBoost was performed on the SFSC data to evaluate the system’s capacity to identify fire-related objects. The results demonstrate that for fire-related objects, XGBoost achieved the highest object identification accuracy, with values of 0.717 and 0.767. This was followed by random forest, with values of 0.468 and 0.510. Finally, we tested YOLOv6 in a simulated fire evacuation scenario to gauge its practicality in emergencies. The results show that YOLOv6 can accurately identify fire-related items in real time within a response time of 0.66 s. Therefore, YOLOv6 is a viable option for fire detection and recognition in Korea. The XGBoost classifier provides the highest accuracy when attempting to identify objects, achieving remarkable results. Furthermore, the system accurately identifies fire-related objects while they are being detected in real-time. This makes YOLOv6 an effective tool to use in fire detection and identification initiatives.

Aiming at the deficiency of reliability design for traditional airborne fire-detecting system, the optimization design is carried out in this paper from four aspects: fire-detecting system architecture, fire detection circuit, data security strategy and built-in self-detection. An improved fire-detecting system architecture is proposed, which effectively reduces the transmission links of fire information and significantly improves the reliability of fire detection and fire alarm tasks. The full bridge conditioning mode is adopted to realize the detection of the full resistance range of the fire sensor, which increases the pollution detection capability of the fire sensor, and a high-reliability BIT detection circuit is designed to realize the online fault diagnosis of the system in the whole working cycle. At the same time, the security data strategy is designed for the information transmission process, which can effectively improve the confidence and reduce the false alarm rate. Both theoretical analysis and test results verify that the reliability of alarm task and the reduction of false alarm rate of the optimized fire-detecting system have been significantly optimized and improved. Furthermore the scheme can improve the accuracy and reliability of the fire-detecting system and enhance the safety of the aircraft.

The rising threat of heat-related and fire incidents underscores the urgent need for advanced thermal and fire detection systems to ensure timely and accurate responses. This report presents a smart fire detection project utilizing the YOLOv5 deep learning model. The project aims to design an early fire detection system with real-time capabilities. The proposed system implements a convolutional neural network (CNN) and the YOLOv5 real-time object identification system, enhancing fire detection through anchor box optimization. In the incident of a fire, the system sends an alert to the user’s Telegram app bot via the Internet of Things (IoT), assisting in taking necessary precautions. The project demonstrates notable efficiency in fire detection and alerting capabilities, with system evaluation metrics showing an F1 score of 95%, mAP@50 of 97%, accuracy 96.3%, and a recall rate of 89%. These results underscore the system's reliability and precision. The project contributes significantly to Sustainable Development Goals (SDG), goal 9 for industry, innovation, and infrastructure, and 11 for sustainable cities and communities highlighting its potential to enhance fire safety measures in various settings.

Multisensor fire detection algorithm (MSFDA) is one of the current important research issues for intelligent building. First, we design a fire fighting robot with extinguish for the intelligent building. The fire fighting robot is constructed using aluminium frame. The contour of the robot is cylinder. The diameter is 40 cm, and height is about 80 cm. There are six systems in the fire fighting robot, including structure, avoidance obstacle and driver system, software development system, fire detection, remote supervise system and others. We design the fire detection system using three flame sensors in the fire fighting robot. The adaptive fusion method is proposed for tire detection of fire fighting robot. We use computer simulation to improve the method to be adequate for fire detection. We design the fire detection system in the fire fighting robot, and program the tire detection and fighting procedure using sensor based method. Finally, we implement the fire detection system using fire fighting robot. If fire accident is true, the robot can find out fire source using the proposed method by fire detection system, and move to fire source to fight the fire using extinguish.

In this paper, we proposed image processing technique for automatic real time fire and smoke detection in tunnel environment. To avoid the large scale of damage of fire occurred in the tunnel, it is necessary to have a system to minimize and to discover the incident as fast as possible. However it is impossible to keep the human observation of Closed-Circuit Television (CCTV) in tunnel for 24 hour. So if the fire and smoke detection system through image processing can warn fire state, it will be very convenient, and it can be possible to minimize damage even when people is not in front of monitor. The fire and smoke detection is different from the forest fire detection as there are elements such as car and tunnel lights and others that are different from the forest environment so that an indigenous algorithm has to be developed. The two algorithms proposed in this paper, are able to detect the exact position, at the earlier stage of incident. In addition, by comparing properties of each algorithm throughout experiment, we have proved the validity and efficiency of proposed algorithm

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

The security of home, laboratory, office, factory and building is important to human life. We develop an intelligent multisensor based security system that contains a fire fighting robot in our daily life. The security system can detect abnormal and dangerous situation and notify us. First, we design a fire fighting robot with extinguisher for the intelligent building. The fire fighting robot is constructed using aluminum frame. The contour of the robot is cylinder. The diameter is 50 cm, and height is about 130 cm. There are six systems in the fire fighting robot, including structure, avoidance obstacle and driver system, software development system, fire detection, remote supervise system and others. We design the fire detection system using two flame sensors in the fire fighting robot. We design the fire detection system in the fire fighting robot, and program the fire detection and fighting procedure using sensor based method. We design a low cost based obstacle detection modular using IR sensor and ultrasonic sensor in the robot. We think that man-machine interface in the fire fighting robot that must has mobility and convenience, we use touch screen to display system status, and design a general user interface (GUI) on the touch screen. Finally, we implement the fire detection system using fire fighting robot. If fire accident is true, the robot can find out fire source using the proposed method by fire detection system, and move to fire source to fight the fire using extinguish.

Indoor fires can easily cause property damage and especially serious casualties. Early and timely fire detection helps firefighters make scientific judgments on the cause of fires, thereby effectively controlling fire accidents. However, most of the existing computer-vision-based fire detection methods are only able to detect a single case of flame or smoke. In this paper, a tailored deep-learning-based scheme is designed to simultaneously detect flame and smoke objects in indoor scenes. We adopt the semantic segmentation architecture DeepLabv3+ as the main model, which is an encoder-decoder architecture for both the detection and segmentation of fire objects. Within this, the key module, e.g., atrous convolution, is integrated into the architecture to improve image resolution and accurately locate targets. In addition, to solve the question of an insufficient indoor fire dataset, we prepare and construct a new annotated dataset named the ‘Flame and Smoke Semantic Dataset (FSSD)’, which includes extensive semantic information of fire objects and is collected from real indoor scenes and other fire sources. Experiments conducted on our FSSD database and the comparisons with state-of-the-art methods (FCN, PSPNet, and DeepLabv3), confirm the high performance of the proposed scheme with 91.53% aAcc, 89.67% mAcc, and 0.8018 mIoU, respectively.