Abstract
A fire is an extraordinary event that can damage property and have a notable effect on people’s lives. However, the early detection of smoke and fire has been identified as a challenge in many recent studies. Therefore, different solutions have been proposed to approach the timely detection of fire events and avoid human casualties. As a solution, we used an affordable visual detection system. This method is possibly effective because early fire detection is recognized. In most developed countries, CCTV surveillance systems are installed in almost every public location to take periodic images of a specific area. Notwithstanding, cameras are used under different types of ambient light, and they experience occlusions, distortions of view, and changes in the resulting images from different camera angles and the different seasons of the year, all of which affect the accuracy of currently established models. To address these problems, we developed an approach based on an attention feature map used in a capsule network designed to classify fire and smoke locations at different distances outdoors, given only an image of a single fire and smoke as input. The proposed model was designed to solve two main limitations of the base capsule network input and the analysis of large-sized images, as well as to compensate the absence of a deep network using an attention-based approach to improve the classification of the fire and smoke results. In term of practicality, our method is comparable with prior strategies based on machine learning and deep learning methods. We trained and tested the proposed model using our datasets collected from different sources. As the results indicate, a high classification accuracy in comparison with other modern architectures was achieved. Further, the results indicate that the proposed approach is robust and stable for the classification of images from outdoor CCTV cameras with different viewpoints given the presence of smoke and fire.
Highlights
Fire detection is considered a challenging yet important task, considering its direct impact on human safety and the environment
Preventing fire events is considered to be of the highest priority owing to unrecoverable damage to populations and even an entire country
As we we use use an an attention attention map feature map, map, we we develop develop aarobust robust capsule capsule network-based network-based approach approach that that takes takes aa lower lower feature layer and routes to a higher layer within a limited spatially local window
Summary
Fire detection is considered a challenging yet important task, considering its direct impact on human safety and the environment. State-of-the-art technology requires appropriate solutions for detecting fires during its earliest possible stage to avoid the possibility of harming human beings [1]. Fire control has always been a challenge to countries around the world. In developing countries, owing to a lack of financial resources required to predict and control the likelihood of such events. Preventing fire events is considered to be of the highest priority owing to unrecoverable damage to populations and even an entire country. Fires can be detected using sensory systems that define changes in the presence of smoke or temperature within a compartment
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