Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

Abstract

While the number of casualties and amount of property damage caused by fires in urban areas are increasing each year, studies on their automatic detection have not maintained pace with the scale of such fire damage. Camera-based fire detection systems have numerous advantages over conventional sensor-based methods, but most research in this area has been limited to daytime use. However, night-time fire detection in urban areas is more difficult to achieve than daytime detection owing to the presence of ambient lighting such as headlights, neon signs, and streetlights. Therefore, in this study, we propose an algorithm that can quickly detect a fire at night in urban areas by reflecting its night-time characteristics. It is termed ELASTIC-YOLOv3 (which is an improvement over the existing YOLOv3) to detect fire candidate areas quickly and accurately, regardless of the size of the fire during the pre-processing stage. To reflect the dynamic characteristics of a night-time flame, N frames are accumulated to create a temporal fire-tube, and a histogram of the optical flow of the flame is extracted from the fire-tube and converted into a bag-of-features (BoF) histogram. The BoF is then applied to a random forest classifier, which achieves a fast classification and high classification performance of the tabular features to verify a fire candidate. Based on a performance comparison against a few other state-of-the-art fire detection methods, the proposed method can increase the fire detection at night compared to deep neural network (DNN)-based methods and achieves a reduced processing time without any loss in accuracy.

Light-Weight Student LSTM for Real-Time Wildfire Smoke Detection

“…Because the wildfire detection system must detect smoke in real time, its performance as well as speed are important factors for consideration. Therefore, we measured the accuracy of the one-stage detector YOLOv3 [ 38 ] and recently proposed a Faster R-CNN [ 31 ], RetinaNet [ 40 ], CornerNet [ 41 ], CenterNet-lite [ 42 ], and ELASTIC-YOLOv3 [ 43 ] object detectors. Experiments were run frame by frame on 24 wildfire smoke and non-smoke test sequences.…”

“…To construct a detector suitable for wildfire smoke, we compared and evaluated various recently proposed CNN-based object detectors, such as Faster-RCNN [ 31 ], RetinaNet [ 40 ], CornerNet [ 41 ], CenterNet [ 42 ], YOLOv3 [ 38 ], and ELASTIC-YOLOv3 [ 43 ]. We then compared two evaluation metrics, i.e., the processing speed and recall, for measuring the missing minimisation.…”

Image Recognition Technology in Texture Identification of Marine Sediment Sonar Image

“…Generally speaking, when the accuracy is high, the recall rate is often on the low side, while the recall rate is high, the accuracy rate will have a downward trend [25]. However, for the evaluation of an algorithm, it is impossible to consider only one aspect of performance, so it is necessary to comprehensively consider the performance measurement of accuracy and recall, so the F1 metric is introduced [26]. F1 measure is the harmonic average of accuracy and recall.…”

Examination of Abnormal Behavior Detection Based on Improved YOLOv3

“…Currently, the object detection algorithm based on deep learning has made great achievements in many fields [9][10][11][12]. YOLOv3 [13][14][15][16] is a typical object detection algorithm based on deep learning. It uses convolutional neural networks (CNN) to complete the detection task and directly returns the position and category of the object.…”