An Improvement of the Fire Detection and Classification Method Using YOLOv3 for Surveillance Systems

Abstract

Currently, sensor-based systems for fire detection are widely used worldwide. Further research has shown that camera-based fire detection systems achieve much better results than sensor-based methods. In this study, we present a method for real-time high-speed fire detection using deep learning. A new special convolutional neural network was developed to detect fire regions using the existing YOLOv3 algorithm. Due to the fact that our real-time fire detector cameras were built on a Banana Pi M3 board, we adapted the YOLOv3 network to the board level. Firstly, we tested the latest versions of YOLO algorithms to select the appropriate algorithm and used it in our study for fire detection. The default versions of the YOLO approach have very low accuracy after training and testing in fire detection cases. We selected the YOLOv3 network to improve and use it for the successful detection and warning of fire disasters. By modifying the algorithm, we recorded the results of a rapid and high-precision detection of fire, during both day and night, irrespective of the shape and size. Another advantage is that the algorithm is capable of detecting fires that are 1 m long and 0.3 m wide at a distance of 50 m. Experimental results showed that the proposed method successfully detected fire candidate areas and achieved a seamless classification performance compared to other conventional fire detection frameworks.

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

“…To evaluate and analyze the effectiveness of fire recognition cases, we compared the proposed approach with recently published fire detection methods. To perform this task, we employed widely used estimation metrics for object (static or dynamic) detection, as in our previous publications [16,56]. First, we computed the precision and recall metrics It employs a CSPNet strategy to partition the feature map of the base layer into two parts and subsequently merges them through a cross-stage hierarchy.…”

“…To evaluate and analyze the effectiveness of fire recognition cases, we compared the proposed approach with recently published fire detection methods. To perform this task, we employed widely used estimation metrics for object (static or dynamic) detection, as in our previous publications [16,56]. First, we computed the precision and recall metrics Experiments have demonstrated that our improved fire detector can relieve people's anxiety, and it enables the early suppression and rapid response irrespective of the time of day or the shape or size of the fire.…”

“…We started our research by examining YOLO networks with the currently available 9200 608 × 608 and 416 × 416 fire images. In our previously published paper [16], we achieved a high accuracy using the YOLOv3 network with images of 608 × 608 resolution. However, the training and testing process takes more time than expected because of the large input images and low frames per second (FPS) values.…”

“…The rest of the manuscript resembles the structure of [16], which was published recently. The key difference is that we changed the main network from YOLOv3 to YOLOv4 to use new features and apply a fire detection area with a down sampled image size.…”

“…We deduced from [53] that the detection of small flame regions is not easy and that most approaches cannot detect them in a timely manner. To overcome this difficulty, we produced small-scale fire images to improve the classification accuracy, as detailed in [16]. We used a large-scale feature map to identify small moving objects and combine them with the feature map in the previous layers, which helped preserve the fine-grained features, as mentioned in [32].…”