Applications of Artificial Intelligence in Fire & Safety | by Vedant Kumar | Dec, 2020


Overview of the applications of AI in mitigating the dangers due to fire

Vedant Kumar

Being a student secretary of the Fire and Security Association of India at my college, I have conducted various surveys and workshops on methodologies to avert the dangers caused due to fire. As an AI enthusiast, I have always aspired to come with innovative solutions to tackle such issues. My experience in the fire and safety domain with competence in building AI solutions motivated me to think of possible solutions to tackle problems faced by the fire department.

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A common problem faced by the fire officials when a rescue operation is underway in a place that has engulfed with flames and smoke is poor visibility. It becomes difficult to notice objects like a door, staircase, or any obstruction in their path of rescue, causing a delay in executing the rescue operation. Such issues are concerning, especially when a life is at stake.

Computer vision process for perceiving objects, Image by author
AI and IOT based solution for fire officials, Image by author

AUDREY strands Assistant for Understanding Data through Reasoning, Extraction, and synthesis. It is an AI solution developed by NASA. In the previous section, we have discussed how some sensors can alleviate a stressful situation by understanding the data from the body of the respondent. However, AUDREY analyzes surrounding data and recommends the safest plan of action. For example, if AUDREY senses a high level of toxic gases like carbon monoxide, it can caution the respondent suggesting him to be more vigilant. Using artificial intelligence, AUDREY can collect data on temperatures, gases, and other danger signals and guide a team of first responders safely through the flames.

Two approaches are used to fight forest fires using AI- image-based and sensor-based.

Image-based approach:

In an image-based approach, a convolutional neural network is trained to detect the fires. The process involves preparing the dataset, annotating the dataset, training the model, and testing the model for validation. The deep learning model is usually deployed on drones which are used for surveillance purposes to detect the presence of wildfires. The task is very similar to that of an object detection model. Once the model has learned the features of the flames from the data and its annotations, it can be used for the detection purpose. The most commonly used model is MobileNet because it offers good accuracy with less computational complexity owing to the use of depth-wise convolution. This can be further extended to detect the severity of the incident by using the k-nearest neighbor algorithm. This approach is similar to what the famous YOLO model uses. The drones detect these flames and warn the authority to take the necessary action. In this way, deep learning is used to mitigate the extremity of the catastrophe.

Sensor-based approach:

In a sensor-based approach, a variety of sensors present in the forest make a cumulative prediction about the occurrence of forest fires. Sensors are used to measure the amount of carbon dioxide, hydrogen sulfide, carbon monoxide, and oxygen in the atmosphere. This data coupled with variations in the surrounding temperature along with the change in humidity level is used for the detection of wildfires. In this approach, the machine learning model is trained on millions of datasets and is competent to make accurate predictions. When an anomaly is detected by the ML model, it raises an alarm to warn the forest officials. Again, classification models like the random forest can be used to detect forest fires. An advanced application of the same concept would be to predict the occurrence of the next forest fires. This is a time series problem and can be solved using deep learning models like Long Short-Term Memory (LSTM) or recurrent neural network (RNN).

Therefore, AI has an enormous potential to grow in the domain of fire and safety. These are just a few applications that can be employed to protect the environment and the lives of the people from a mishap.

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