Computer Vision Application for Early Stage Smoke Detection on Ships

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.

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

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.

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

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.

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.

The overall results of this research addressed the question of how increased time for mineworkers to evacuate mines prior to the onset of untenable fire conditions is achievable. Additionally, improved asset loss control for underground mining is feasible through the successful development of Video Based Fire Detection (VBFD) in mining, as it provides more information earlier for mine fire fighters to intervene in the growth and spread of fire, therefore maintainable coal production is achievable. VBFD may also be used to activate automatic fire suppression systems. VBFD is an emerging volume type smoke detection technology that utilises Closed Circuit Television (CCTV) cameras to capture and analyse real time video data for the purpose of smoke recognition. VBFD technology performance was compared with the performance of fire detection methods currently used in underground coalmines to determine whether valuable improvements to fire safety and asset loss control could be made. Increased available safe evacuation time for mineworkers and a longer available time to extinguish fires earlier in their growth stage were considered the primary measures of success. The scope of this research included three main studies: a. Proof Of Concept (POC) tests; b. Detailed Experiments; and c. Field Testing. The initial trials successfully demonstrated the capability of VBFD to detect small coal fires associated with fixed plant installations in a low light simulated mine environment. Encouraged by these findings, detailed experiments were developed and subsequently applied at the Safety In Mines Testing and Research Station (SIMTARS) experimental facility at Redbank, Australia. The experimental plan involved 54 individual fire test experiments that recorded the level of Carbon Monoxide (CO) concurrent at the VBFD alarm activation time. Sensitivity analysis was undertaken by varying the air velocity over the fire source, which affected the smoke plume shape. The other component of the sensitivity analysis involved variations in light levels aimed at testing the VBFD smoke detection sensitivity. The data were subsequently analysed to identify trends in the detection response of both the CO sensors and the VBFD. Some of the tests carried out exceeded the capability of the VBFD system, which was an important aspect in the research and the evolving capability of video smoke detection. The experimental fire characteristics were subsequently replicated numerically using Computational Fluid Dynamics (CFD) so it could be used in this research to estimate the evacuation response of mineworkers under more onerous fire conditions, which could not physically be undertaken in an operational mine. A following VBFD field study was proposed to review and utilise the results of installation commissioning data and maintenance testing of a VBFD system at an operational underground mine environment. The purpose of this field-testing was to inform the underground mining industry of limitations that may exist in terms of the VBFD reliability in service, such as potential maintenance issues. During the period of the subject research the Australian resources industry moved from being in a position to readily facilitate and support minor external unfunded research projects within its underground mine assets, to its current position where it was no longer willing or able to assist. As a result of the lack of a trial VBFD system in an underground mine, the subsequent field study opportunistically considered the reliability and performance of VBFD in a very similar and related environment, however this facility was not an underground mine, but a South African coal fired power station. The results of the reliability testing of 360 VBFD devices over a 12 months’ period showed that VBFD was very reliable. Future VBFD research might address two aspects. Firstly, VBFD may be used to detect flames or smoke. Whilst coal handling plant fires typically manifest during their early stages of growth as smouldering fires, other fixed plant fires have the potential to produce flames from the outset. Underground refuelling stations and transformers exhibit this phenomenon, so flame detection capability is possible and should be explored. In this research, VBFD CCTV cameras were of a very basic functionality. They were simple, relatively low cost devices operating in the visible light spectrum only. Secondly, the use of high resolution “mega pixel” cameras with the capability to operate in very low light levels should be researched. It was found that VBFD offers considerably faster response to visible smoke plumes than other forms of fire detection currently used in underground coalmines. In terms of VBFD reliability, it was more resistant to contamination from mine pollutants than typical commercial ‘point type’ photoelectric or ionisation smoke detectors. It was shown in the research that significant improvements in fire safety and asset loss control in underground mining can be made through the application of VBFD for early fire detection of fixed plant fires.

Currently, the forest fires are worldwide problem which requires a complete solution. The consequences of it are related, not only, to the environmental and biodiversity destruction but also to human and economic losses. In this article, forest fire and smoke detection systems, methods and technics literature review are carried out. To accomplish this aim, a generic mobile and non-mobile monitoring and surveillance systems applied in this field analysis took place. From the review carried out, it has been found that for fire detection two types of methods have been used, one based on machine learning and another through color features extraction of images or video frames, on the other hand, the combination of both methods is used for the detection of smoke and fire or only smoke, this guarantees accurate rates of more than 90%. Finally, a detailed analysis of the use of these fire and smoke detection algorithms in the UAS/UAV’s systems was carried out paying special attention on them due to the flexibility, versatility, and maneuverability abilities among others displayed by these systems that help prevent and mitigate forest fires.KeywordsFire forestSmoke forestMachine learningColor features extraction

Smoke control is one of the important aspects in atrium fire. For an efficient smoke control strategy, it is very important to identify the smoke and fire source in a very short period of time. However, traditional methods such as point type detectors are not effective for smoke and fire detection in large space such as atrium. Therefore, video smoke and fire detection systems are proposed. For the development of the system, automatic extraction and tracking of flame are two important problems needed to be solved. Based on entropy theory, region growing and Otsu method, a new automatic integrated algorithm, which is used to track flame from video images, is proposed in this paper. It can successfully identify flames from different environment, different background and in different form. The experimental results show that this integrated algorithm has stronger robustness and wider adaptability. In addition, because of the low computational demand of this algorithm, it is also possible to be used as part of a robust, real-time smoke and fire detection system.

Flame detection is a specialized task in fire detection and forest fire monitoring systems. In this paper, a static and dynamic texture analysis of flame in forest fire detection is proposed. The flames are initially segmented, based on the color in YCbCr (luminance, chrominance blue and chrominance red components) color space called candidate flame region. From the candidate flame region, the static and dynamic texture features are extracted. Static texture features are obtained by hybrid texture descriptors. Dynamic texture features are derived using 2D wavelet decomposition in temporal domain and 3D volumetric wavelet decomposition. Finally, extreme learning machine classifier is used to classify the candidate flame region as real flame or non-flame, based on the extracted texture features. The proposed flame detection system is applied to various fire detection scenes, in the real environments and it effectively distinguishes fire from fire-colored moving objects. The results show that the proposed fire detection technique achieves the average detection rate of 95.65% which is better compared to other state-of-art methods.

Multi-criteria fire detection systems using a probabilistic neural network

We consider the possibilities for designing a new hardware and software system for fire detection, based on combination of video analytics technology and analysis of IR signals from sensors in the mid-IR and thermal IR ranges. We consider the parameters (in particular, the maximum distance for detection of the seat of a fire) for IR sensors based on various photodetectors. As a detector in the visible range, designed for early smoke or open flame detection based on analysis of sequential images of a scene, we suggest using a standard IP network camera.

A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.

<strong class="journal-contentHeaderColor">Abstract.</strong> With the advancement of computer vision, artificial intelligence, and remote sensing technologies, deep learning algorithms are increasingly used in terrestrial, airborne, and spaceborne-based fire detection systems. The performance and generalization of these data-driven fire detection algorithms, however, are restricted by the limited number and source of fire detection datasets. A large-scale fire detection benchmark dataset covering complex and varied fire scenarios is urgently needed. This work constructs a 100,000-level Flame and Smoke Detection Dataset (FASDD) based on multi-source heterogeneous flame and smoke images. It holds rich variations in image size, resolution, illumination (day and night), scenario (indoor and outdoor), image range (far and near), viewing angle (top view and side view), platform (surveillance cameras, drones, and satellites), and data source (Internet, social media, and open-access fire datasets). To the best of our knowledge, FASDD is currently the most versatile and comprehensive dataset for fire detection. It provides a challenging benchmark to drive the continuous evolution of fire detection models. Additionally, we formulate a unified workflow for preprocessing, annotation, and quality control of fire samples. Out-of-the-box annotations are published in four different formats for training deep learning models. Extensive performance evaluations based on classical methods show that most of the object detection models trained on FASDD can achieve satisfactory fire detection results, and especially YOLOv5x achieves nearly 80 % mAP@0.5 accuracy on heterogeneous images spanning two domains of computer vision and remote sensing. And the application in wildfire location demonstrates that deep learning models trained on our dataset can be used in recognizing and monitoring forest fires. Deep learning models trained with FASDD can be simultaneously deployed on satellites, drones, and ground sensors, thus realizing collaborative fire observation and detection in a space-air-ground integrated network environment. The dataset is available from the Science Data Bank website at <a href="https://doi.org/10.57760/sciencedb.j00104.00103" target="_blank" rel="noopener">https://doi.org/10.57760/sciencedb.j00104.00103</a> (Wang et al., 2022a).