Journal of Information Technology and Computer Science Volume 8, Number 2, August 2023, pp.86-97
Journal Homepage: www.jitecs.ub.ac.id
Advancements in Fire Alarm Detection using Computer Vision and Machine Learning: A Literature Review
M Fadli Ridhani*1, Wayan Firdaus Mahmudy2
1,2Brawijaya University, Malang
1[email protected], 2[email protected]
*Corresponding Author
Received 10 August 2023; accepted 23 August 2023
Abstract. Fire is one of the most common and increasing emergencies that threaten public safety and social development. This can cause significant loss of life and damage. Fire detection systems play an important role in the early detection of fires. The purpose of this study is to provide a brief survey of the latest literature in the field, which can provide a foundation for researchers to develop a Fire Alarm Detection System with a Computer Vision and Machine Learning approach. The Computer Vision and Machine Learning approaches are popular and have been extensively studied because the advantages. The main challenges in fire detection systems are high false alarm rates and slow response times. This research presents potentials and emerging trends through Computer Vision and Machine Learning approaches for Fire Alarm Detection Systems in the future, including the selection of input features to the use of appropriate methods and the process flow of Fire Alarm Detection Systems.
Keywords: fire alarm detection, computer vision, machine learning
1. Introduction
Fire is one of the most common emergencies [1], continues to increase [2] and threatens public safety and social development [3]. Fires can claim many lives [4] and cause significant property damage [5] [6], which is why fire incidents are a major concern worldwide [7] not only indoors but also outdoors [2]. In handling fire threats, Fire Alarm Detection Systems play an important role [8] in early detection of fire [9]
to provide timely alerts to occupants or authorities [10], so that for a fast evacuation [11]. There are two main challenges that researchers focus on in current Fire Alarm Detection Systems, namely, the high false alarm rate [12][13] and slow response times.
For example, in the United Kingdom, the high false alarm rate based on statistical1 data on fire and rescue incidents published by the U.K Home Office shows that of the 555,795 incidents handled by the fire service in 2019, 41% of them were recorded as false alarms, resulting in substantial losses in terms of human and financial resources [9].
Fire detection systems play an important role in preventing more severe fires and minimizing the impact on the environment and human life [14]. The development of Computer Vision and Machine Learning-based fire detection technology has brought significant advancements in reducing false alarms and shortening response times [15].
1 Available: UK home office, Fire & rescue incident statistics, England, year ending December 2019.
M Fadli et al., Advancements in Fire Alarm Detection … 87 Computer Vision technology enables fire detection systems to visually detect fire- related events such as smoke or flames [15][16] using visual sensors, besides that computer vision is also used in other fields such as eye movement [17], health [18] and more. Meanwhile, Machine Learning allows the system to learn and recognize fire patterns through data-driven learning processes [9] using fusion sensor. Recent research on Fire Alarm Detection Systems using Computer Vision and Machine Learning has shown significant progress [13]. The findings of this study indicate that this approach has the potential to improve accuracy to minimize false alarms and reduce response times.
The importance of conducting a literature review to understand the last advancements in Fire Alarm Detection Systems using Computer Vision and Machine Learning is very important for the development of more reliable and accurate fire detection systems [19]. Through a literature review, a comprehensive overview of the two approaches, techniques and methods developed in the related literature can be obtained. The authors conducted a thorough review of the last advancements in Fire Alarm Detection Systems using Computer Vision and Machine Learning. They analyzed various algorithms and models employed and evaluated their strengths (effectiveness) and limitations [20]. By researching and understanding the relevant literature, it is expected that more reliable and accurate fire detection systems using Computer Vision and Machine Learning can be developed. This papers also explains how Computer Vision and Machine Learning are used for Fire Alarm Detection System from the selection of input features to the use of appropriate methods, thus providing a solid foundation for the field more broadly. This can contribute to improving environmental safety and security as well as minimizing losses due to fire [21]. It is divided into five sections: Section 1 provides an introduction, Section 2 explains the methodology used in reviewing the relevant articles, Section 3 describes the obtained review results, Section 4 presents a discussion on the application of Computer Vision and Machine Learning approaches in Fire Alarm Detection Systems, and finally, Section 5 presents the conclusion of the literature review conducted and provides recommendations for future research.
2. Methodology
There are three stages in finding relevant literature for this research. First, a specific searche are conducted through Google Scholar, determining keywords that correspond to the queries "Fire Alarm Detection using Computer Vision" and "Fire Alarm Detection using Machine Learning". Second, the search is minimized using a boolean operator, limiting results to articles published between 2013 and 2023. Finally, articles with research-relevant titles are identified, and further filtering is performed by examining specific content, especially in the abstract section [22].
3. Results
3.1 Fire Alarm Detection System
It is a very important system [7][23], specially designed for fire detection to identify [24] fire incidents at an early stage. This system aims to provide warning [25]
to occupants or authorized personnel, fast evacuation [11] and effective fire management. The Fire Alarm Detection System is used in two environments, namely indoor and outdoor [2][26]. The use of the Fire Alarm Detection Systems indoors, for example at home, school, office, and other similar locations [6][27]. Then, use the Fire Alarm Detection System outdoors, for example, in forests [28][29][30][31][32] and tunnels [19]. Various types of sensors [33] have been introduced in Fire Alarm
88 Journal Volume 8, Number 2, August 2023, pp 85-97 Detection Systems, with two common types being: First, visual sensors developed based on smoke and fire detection [33][34][35]. Second, fusion sensor: Originally developed using a single sensor configuration, multi-sensor detectors are now available that measure physical characteristics of fire such as light, temperature, smoke, gas and humidity [36][37][38]. Ding et al. using a multi-sensor approach to monitoring light, smoke, temperature, gas, and humidity [25]. Fusion sensors have limitations, such as having to be close to a source of fire [39], and not being effective enough in open areas [40][41][42]. There are also visual and fusion sensor combinations [43], as demonstrated by Jiang et al., which combines (visual) smoke detection and (fusion) CO2 gas detection [44].
3.2 Fire Detection Using Computer Vision
Computer Vision is also part of Machine Learning, but it is specialized for image (picture and video) processing [45]. The use of Computer Vision in Fire Alarm Detection is gaining popularity [46], and has been extensively researched for its advantages [47] automatically detecting fires by identifying visual cues such as smoke and fire captured by vision sensors [26]. Fire detection algorithms utilize image processing techniques [48] (segmentation) in Computer Vision to remove visual elements related to fire or smoke from the background, with for example Convolutional Neural Network (CNN) providing good performance and effectiveness [49] in image processing. Various segmentation methods, including thresholding, image processing, or color-based algorithms [33] [50] [51] [52] [53], are used to extract features relevant to recognizing objects such as smoke or fire. Currently, visual sensors are widely used in various environments [54]. The use of Computer Vision can be a solution limitations of fusion sensor, such as slow response times [55]. The visual inspection approach has advantages in outdoor (forest) fire detection, however computer vision uses deep learning models, which usually have slow response times due to the complexity of handling image data and the constraints of obtaining clear images of smoke or fire [56].
Computer Vision-based fire detection systems currently can only detect one type of fire or smoke [14].
3.3 Fire Detection Using Machine Learning
Application of Machine Learning in Fire Alarm Detection has effective handling of all parameters by understanding their relationship in classification. Various models have been developed for effective fire detection, with fusion sensor-based fire detection systems heavily relying on Machine Learning [9]. In a study [38] multi-fusion sensor was employed using readings from smoke, temperature, and humidity sensors, where Neuro Fuzzy Logic was used as a model to determine fire incidents. Machine Learning implementations, such as Support Vector Machine (SVM), do not require high computation compared to deep learning models which require more resources. Three popular Machine Learning approaches, namely SVM, RF, and K-Means, have been implemented. The main concept in SVM is to find an optimal hyperplane that separates instances from different classes by maximizing the margin, with kernel tricks playing a crucial role in transferring the input space from low to high dimensions, thus enabling better accuracy [9].
3.4 Fire Alarm Detection System Process Flow
So that it can be easily understood and has a clear picture of the process flow of the Fire Alarm Detection System, can be seen in Figure1, as follows:
M Fadli et al., Advancements in Fire Alarm Detection … 89
Figure 1. Fire Alarm Detection System Process Flow.
The process flow of a Fire Alarm Detection System can be visualized in Figure 1. Usually visual sensors are used for outdoor environments such as forests, tunnels, and others. In the "object" section, drone sensors, cameras, CCTV and satellites are used to take pictures or videos. Then, sensor fusion is usually used in indoor environments such as homes, schools, offices and others. Arduino/IoT devices are used as fusion sensors to collect temperature, gas and other data. After the data is collected by the sensor, the visual sensor provides image or video data, while the fusion sensor produces tabular data. The next step is to use computer vision for image data and machine learning for tabular data. It is very important to choose input features from the relevant data because it will affect the results [18] and using a method that fits the data will also have a significant effect on the model that is built [57]. Computer vision is a subset of machine learning but is specifically focused on processing image data.
Computer vision and machine learning in general have the same stages, namely data preprocessing, feature extraction, model training, and model evaluation. After the model evaluation stage, the appropriate model for the Fire Alarm Detection System is selected. When a fire occurs, the sensors take a data from the object (environment) and process it through the trained model. The output of this process indicates whether there is a fire or not. Subsequently, the system triggers a warning by sending notifications to residents. In some cases, the alarm sound may be activated, and notifications can be sent through mobile phones or computers.
4 Discussion
Table 1. Sensor-Based Environmental Monitoring Algorithms And Techniques
Year Sensor Environment Algorithm Accuracy Refs.
2013 (VS)2 - Support Vector Machine (SVM) - [58]
2016 (FS)3 Indoor Fuzzy Logic - [59]
2017 (FS) Indoor Probabilistic Neural Network
(PNN) - [60]
2018 (VS) In/Out Convolutional Neural Network
(CNN) 94.39% [61]
2 Visual Sensor
3 Fusion Sensor
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Year Sensor Environment Algorithm Accuracy Refs.
2018 (VS) Outdoor Deep Convolutional Neural
Network (DCNN) 98% [30]
2018 (VS) - PSO+ K-Medoids Clustering - [46]
2019 (VS) - Deep Convolutional Neural
Network (DCNN) 99.24% [62]
2019 (FS) - Trend Predictive Neural Network
(TPNN) 99% [63]
2019 (FS) Indoor K-Nearest Neighbors (KNN) 99.71% [64]
2019 (FS) - Adaptive Neuro-Fuzzy Inference
System (ANFIS) - [38]
2020 (VS) - Deep Convolutional Neural
Network (DCNN) 92.5% [65]
2020 (FS) - Sugeno Fuzzy Logic - [66]
2021 (FS) Indoor Back Propagation Neural Network
(BPNN) 99.4% [20]
2022 (VS) - UFS-Net+CNNs 98.80% [14]
2022 (VS) Indoor Convolution Neural Network
(CNN) + RGB 99.1% [16]
2022 (FS) Indoor Support Vector Machine (SVM) 99.83% [9]
2023 (FS) Indoor Support Vector Classification - [15]
2023 (VS) - Inception Convolution Neural
Network (Incep-V3) 98.68% [67]
The following are the results and a brief discussion of the studies listed in Table 1, categorized based on sensor use: Visual Sensor (VS): In 2013, a study used Visual Sensor (VS) with the Support Vector Machine (SVM) algorithm for fire detection. In 2018, a study utilized Visual Sensors (VS) either indoors or outdoors (in/out) by implementing Convolutional Neural Networks (CNN) and Deep Convolutional Neural Networks (DCNN). In 2019, a study using Visual Sensor (VS) without specifying a specific environment, used the Deep Convolutional Neural Network (DCNN) algorithm. In 2023, a study uses Visual Sensor (VS) without specifying a specific environment, using the Inception Convolution Neural Network (Incep-V3) algorithm.
Fusion Sensor (FS): In 2016, a study used Fusion Sensor (FS) in an indoor environment, using a Fuzzy Logic algorithm. In 2017, a study also utilized Fusion Sensor (FS) in indoor environments, using a Probabilistic Neural Network (PNN) algorithm. As of 2019, several studies have used Fusion Sensors (FS) without specifying a specific environment. Some of the algorithms used include Trend Predictive Neural Network (TPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). In 2021, a study uses Fusion Sensor (FS) in indoor environments, utilizing the Back Propagation Neural Network (BPNN) algorithm. In 2022, a study uses Fusion Sensor (FS) in an indoor environment, using a Support Vector Machine (SVM) algorithm. In 2023, a study uses Fusion Sensor (FS) in indoor environments, using the Support Vector Classification algorithm.
Through this study, the use of Visual sensors (VS) and Fusion sensors (FS) has been explored and evaluated for fire detection. There are variations of the algorithm used, such as Support Vector Machine (SVM), Convolutional Neural Network (CNN),
M Fadli et al., Advancements in Fire Alarm Detection … 91 Deep Convolutional Neural Network (DCNN), Fuzzy Logic, Probabilistic Neural Network (PNN), Trend Predictive Neural Network (TPNN), Adaptive Neuro- Fuzzy Inference System (ANFIS), Back Propagation Neural Network (BPNN), and Support Vector Classification. However, further research is still needed to develop fire detection methods that are more effective and responsive in various environments (indoor and outdoor).
The study results show that the Fire Alarm Detection System can be implemented using visual sensors or sensor fusion, depending on the environment and specific needs. The use of Computer Vision in fire detection offers advantages in detecting visual signals such as smoke and fire which are commonly used in outdoor environments, although there are still challenges in terms of response time and image quality. On the other hand, Machine Learning offers an effective approach to dealing with data generated from gases and temperatures, more suitable for indoor environments.
This research also reveals some limitations of the approach used, such as sensor fusion limitations in terms of range and effectiveness in open areas. The use of deep learning models for Computer Vision-based fire detection also has limitations in terms of slow response times and difficulties in obtaining clear images of smoke or fire.
Therefore, further research is needed to overcome these obstacles and develop methods that are more accurate and responsive in fire detection. The application of Machine Learning in fire detection can be a more efficient alternative compared to deep learning models which require higher computational resources. A Machine Learning approach can help with fire classification by extracting information from maximum margins and leveraging kernel tricks to improve accuracy. By combining the right sensors and using the right methods, a Fire Alarm Detection System can help reduce fire risk and increase environmental safety.
We can see that each approach has its advantages and disadvantages. The researchers did not mention the disadvantages in their research. The disadvantages in computer vision in the Fire Alarm Detection System were not mentioned in the paper.
Conversely, the disadvantages of machine learning are also not mentioned. They only mentioned the advantages of each approach used, and they actually stated the disadvantages of other approaches that were different from their paper. So, from the results of our review by comparing the two approaches, we summarize all the arguments presented, the advantages and disadvantages of computer vision and machine learning, to make it easier to compare. Overall, we summarize through table 2, as follows:
Table 2. Comparison of Computer Vision and Machine Learning approaches for sensor-based Fire Detection
Criteria Computer Vision Machine Learning
Sensor
Type Visual sensors (cameras, CCTV) [33] Fusion sensor (Arduino/IoT) [36]
Usage Outdoor (forest, tunnel) [28] Indoor (home, school, office) [6]
Input Image (smoke and fire) [45]
Tabular data (light, temperature, smoke, gas and humidity) [9]
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Criteria Computer Vision Machine Learning
Detection Speed Instantly detect point (fire) It takes time for (temperature) to reach the sensor [39]
Detection Accuracy
The fire is not clearly visible and can only detect one type of fire or smoke [14]
The temperature change is detected by the sensor
Model
Time High complexity in processing images [56]
Only process data (tabular data)
Computation High (complex models) [56] Low (machine learning) [9]
Accuracy Model complexity [56] Depends on the amount of data used [9]
In the comparison between Computer Vision and Machine Learning in Table 2, there are several key differences. In terms of sensors, Computer Vision uses visual sensors such as cameras and CCTV, while Machine Learning utilizes fusion sensor through the Arduino/IoT. The use of Computer Vision is more prevalent outdoors, such as in forests and tunnels, while Machine Learning is more commonly used indoors, such as in homes, schools, and offices. Regarding inputs, Computer Vision processes images, including smoke and fire, while Machine Learning processes data such as temperature and gas. The detection speed also differs, where Computer Vision can directly detect fire points visually, while Machine Learning requires time for the temperature to reach the sensor before detecting a fire. Detection accuracy also varies between the two. In this case, Computer Vision faces challenges when the fire is not visible in the images, while Machine Learning can recognize temperature changes through the sensors used.
In terms of models, Computer Vision has a higher complexity in processing images, whereas Machine Learning processes data with simpler Machine Learning or Deep Learning models. In terms of computation, Computer Vision requires high computational power due to the use of complex models, while Machine Learning has lower computational requirements. Accuracy also differs between the two, where the complexity of the model in Computer Vision can affect accuracy, while the accuracy of Machine Learning depends on the amount of data used for training the model.
Considering these differences, the choice between Computer Vision and Machine Learning in a fire detection system should be based on the specific needs of the usage environment and the desired fire detection objectives.
Although each type of sensor tends to be used in their respective environments.
Visual sensors tend to be used in outdoor environments and fusion sensors are used in indoor environments. From our analysis of previous articles, we found no use of fusion sensor in outdoor environments. However, visual sensors are not only used in outdoor environments but also indoors. Farajzadeh et al stated that visual sensors have a wider use with various types of environments [54]. This is also done by Muhammad et al who used visual sensors in their research with two environments, namely indoor and outdoor [61]. The use of visual sensors for both indoor and outdoor environments is still very rare, and whether the fusion sensor allows for use in outdoor environments.
In research conducted by Kong et al, he stated that computer vision can be a solution to the limitations of sensor fusion in dealing with slow response times because sensor fusion must be close to the fire, sensor fusion requires enough time for temperature changes to be detected by the sensor [55]. In contrast to visual sensors that only take pictures of smoke or fire from a fire incident. However, this statement was
M Fadli et al., Advancements in Fire Alarm Detection … 93 contradicted by Gaur et al, who stated that computer vision has a much slower response time due to the complexity of handling images [56]. Strengthened by the arguments of Hosseini et al who explained that the limitations of computer vision are challenges in dealing with images that are not clearly visible [14].
The use Computer Vision given affect long time for image processing and high computation because complexity model. For example, that is conducted by Chen et al (2022) comparison Support Vector Machine (SVM) with running time (s) 2.79 and Artificial Neural Network (ANN) with running time (s) 3.83 on the same Fire Alarm Detection System dataset. The result from the model is not much different, SVM with F1-score (99.94%) and ANN with F1-score (99.96%) [9]. So, this is a to the use of appropriate methods. The next to be able to provide better accuracy results, of course, really depends on the data used, choosing the appropriate input features and methods.
Of the two approaches, using either computer vision or machine learning, we cannot make a decision which is better. Because each approach has its own arguments.
However, through the results and discussion in this paper, we believe that this can be a reference and consideration in developing a more reliable and effective Fire Alarm Detection System in the future.
5 Conclusion
This literature review has examined advances in fire detection using Computer Vision and Machine Learning. This study identified that Fire Alarm Detection Systems play an important role in preventing severe fires and reducing fire related losses. Fire Alarm Detection Systems can be implemented using visual sensors or sensor fusion, depending on the specific environment and requirements. The use of Computer Vision in fire detection allows identification of visual cues such as smoke and flames.
However, there are still challenges that need to be overcome, such as slow response times and poor image quality. On the other hand, the use of Machine Learning offers an effective approach for handling variables and classifying fires. However, this study also revealed some limitations in the approach that has been used. Fusion sensors have limitations in terms of range and effectiveness in open areas. The use of deep learning models for Computer Vision-based fire detection still has limitations in terms of slow response times and difficulties in obtaining clear images of smoke or fire. In conclusion, computer vision or machine learning approaches have advantages and disadvantages and are used in each environment. Future Fire Alarm Detection Systems are expected to cover both indoor and outdoor environments with a more effective selection of features and models. With the hope that in the future there will be no use of separate systems and models, this can be a more effective (reliable, accurate and responsive) Fire Alarm Detection System approach.
Acknowledgment
This research is supported by Computer Science Faculty, University of Brawijaya as the funding sponsor, Center for Higher Education Funding (BPPT) and Indonesia Endowment Funds for Education (LPDP).
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