Fire Detection From Image and Video Using YOLOv5

For the detection of fire-like targets in indoor, outdoor and forest fire images, as well as fire detection under different natural lights, an improved YOLOv5 fire detection deep learning algorithm is proposed. The YOLOv5 detection model expands the feature extraction network from three dimensions, which enhances feature propagation of fire small targets identification, improves network performance, and reduces model parameters. Furthermore, through the promotion of the feature pyramid, the top-performing prediction box is obtained. Fire-YOLOv5 attains excellent results compared to state-of-the-art object detection networks, notably in the detection of small targets of fire and smoke with mAP 90.5% and f1 score 88%. Overall, the Fire-YOLOv5 detection model can effectively deal with the inspection of small fire targets, as well as fire-like and smoke-like objects with F1 score 0.88. When the input image size is 416 x 416 resolution, the average detection time is 0.12 s per frame, which can provide real-time forest fire detection. Moreover, the algorithm proposed in this paper can also be applied to small target detection under other complicated situations. The proposed system shows an improved approach in all fire detection metrics such as precision, recall, and mean average precision.Comment: 6 pages, 6 sections, unpublished pape

Fire Early Warning Using Fire Sensors, Microcontroller and SMS Gateway

A fire disaster that does not save can certainly cause losses, both in the form of objects and casualties. This occurs for several reasons: late information obtained from the fire department or the owner's ignorance at the time of a fire. In this study, a fire early detection system was built using smoke, heat, and gas sensors based on an SMS gateway and an alarm. This system is used to provide information about fire detection as early as possible to protect against fire disasters. With this system, the potential and risk of fire can be reduced. This system is used to identify potential fires that occur in housing. Several experiments were carried out with fire simulations to get the reaction from the sensors used. Covers smoke testing, temperature testing, gas testing, and SMS message responses from various providers. This research produces a fire early warning system that provides SMS and alarm alerts

Comparative study on machine learning algorithms for early fire forest detection system using geodata

Forest fires have caused considerable losses to ecologies, societies and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, a competitive spatial prediction model for automatic early detection of wild forest fire using machine learning algorithms can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps

AN IMPROVED SURVEILLANCE SYSTEM ARCHITECTURE BASED ON FLAME DETECTION IN CCTVS

CCTV is an important part of the surveillance systems. While a person observes the screens with the naked eye in existing CCTV monitoring systems, it is possible that the flame and events are not detected opportunely due to eye fatigue and human carelessness. In this paper, we propose a real-time flame detection method and design architecture of surveillance system to achieve early fire detection. When flame is detected, this system displays the video sequence from the corresponding camera channel on the whole screen with the alarm signal

Kesiapsiagaan Masyarakat dalam Menghadapi Ancaman Kebakaran

Fire disasters still occur frequently so that people are encouraged to have knowledge of preparedness in anticipating fire disasters. However, information about the community's ability to anticipate fire disasters is still very limited. The research objective was to determine community preparedness in facing the threat of fire in Penggilingan Village, Cakung District, East Jakarta. The method used is descriptive research using quantitative data. The study population consisted of all heads of households living in Penggilingan sub-district, totaling 40,641 families, while the sample was part of the heads of families, totaling 125 families. Sampling used proportional random sampling technique. The research variable was community preparedness in facing the threat of fire as measured by 4 parameters from LIPI-UNESCO/ISDR, 2006 namely knowledge and attitude, disaster warning system, emergency response plan, and ability to mobilize resources. Data were analyzed using frequency distribution tables and preparedness indexes. The results showed that the community is prepared to face the threat of fire in Penggilingan Village, Kec. Cakung is categorized as ready with an index value of 79.03. Even though it is in the ready category, it would be better if this preparedness continues to be improved to become very ready. Especially preparedness regarding the disaster warning system and the ability to mobilize resources that still need to be evaluated by the community and stakeholders. As well as fire socialization or training programs should be further intensified so that information about fire disasters can be received by all levels of society.