Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

Abstract

In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People

“…However, the training dataset is constrained using this approach for fire detection. To address this, we used picture augmentation techniques and various computer vision techniques to enhance the number of fire frames [ 9 , 11 ].…”

“…Therefore, we rotated each original fire image and then flipped each rotated image horizontally to increase the number of images in the fire-detection dataset, as explained in detail in the refs. [ 9 , 11 , 12 ]. By applying the data augmentation methods to the original 7395 fire images, we increased the total number of images to 90,700.…”

“…In summary, the main contributions of this study are as follows: Most traditional methods use sensor-based technologies to detect fire scenes [ 3 , 4 , 5 , 6 ]; however, these technologies depend on environmental and illumination changes. Further research has shown that camera-based fire-detection systems achieve much better results with high prediction accuracy, low cost, and reduced processing time to enhance fire safety [ 7 , 8 , 9 , 10 ]. AI-based fire detection is a potentially powerful approach to detect flames and to warn building occupants in different indoor environments because it is highly distinctive, does not depend on colors, size, or shape, and is robust to illumination changes [ 11 , 12 ].…”

“…Most traditional methods use sensor-based technologies to detect fire scenes [ 3 , 4 , 5 , 6 ]; however, these technologies depend on environmental and illumination changes. Further research has shown that camera-based fire-detection systems achieve much better results with high prediction accuracy, low cost, and reduced processing time to enhance fire safety [ 7 , 8 , 9 , 10 ].…”

Automatic Speech Recognition Method Based on Deep Learning Approaches for Uzbek Language

“…ASR is an active and essential research area owing to its wide range of applications, such as security [ 2 ], education [ 3 ], smart healthcare [ 4 , 5 ], and smart cities [ 6 ], as well as the development of interfaces and computing instruments that can enable voice processing. It is a combination of various approaches that assist in the conversion of acoustic data into text, using text matching applied to the detected speech signal occurring in the result.…”