Information-Guided Flame Detection Based on Faster R-CNN

Abstract

Due to the diversity of the shape and texture of flame, and interference objects that similar to flame in color, detecting the position of flame from images is a difficult task. To enable generic object detection methods to achieve better performance in flame detection tasks, a color-guided anchoring strategy is proposed that uses color features of the flame to limit the location of the anchor. To solve the problem of high false alarm rate when directly using generic object detection methods in flame detection, a global information-guided flame detection method is proposed, this strategy uses a parallel network to generate image global information. We use these two methods to improve Faster R-CNN (Regions with Convolutional Neural Network features) to perform the fire detection process in a guided manner. Experiments on the BoWFire dataset show that our method improved detection speed by 10.1% compared with the original Faster R-CNN. In addition, the false alarm rate is decreased by 21.5%, and the overall accuracy of detection is increased by 9.3%. Experiments on PascalVOC and Corsician datasets further demonstrate the robustness of the proposed methods.

Tackling Small Data Challenges in Visual Fire Detection: A Deep Convolutional Generative Adversarial Network Approach

“…They are essential for monitoring both indoors and outdoors for fire signatures such as smoke, heat, and radiation, and to identify early signs of fires to trigger appropriate responses. Significant progress has been made with these technologies in the last decades in part due to advances in sensor design and related technology [1][2][3][4][5][6][7][8]. Nonetheless, important challenges with fire detection remain, and these can roughly be subsumed under two broad headings, insufficient sensitivity on the one hand, and elevated false alarm rates on the other hand [9].…”

Automatic Smoke Detection Based on SLIC-DBSCAN Enhanced Convolutional Neural Network

“…With the rapid development of artificial intelligent (AI) models [25], many researches applied AI models to detect abnormal conditions for mechanical and civil engineering [26][27][28][29][30][31] as well as fire detection domain [32][33][34][35][36][37][38][39]. Recently, Yuan et al proposed a smoke density estimation network.…”

“…Faster region-based convolutional neural network (CNN) and long short-term memory (LSTM) are employed to detect the suspected regions of fire (SRoFs) and classify whether there is a fire or not in a short-term period [35]. Meanwhile, CNN was extended to the fire detection process using generic object detection methods [36]. Gagliardi and Saponara.…”

Computationally Efficient Wildfire Detection Method Using a Deep Convolutional Network Pruned via Fourier Analysis

“…For example, Zhao et al designed a 15-layer convolutional neural network (CNN) to detect the forest fire [25]. What is more, to locate the fire and smoke in frames, Barmpoutis et al [29], Huang et al [30] and Chaoxia et al [31] proposed R-CNN -based fire detection method. However, neural network training requires huge amounts of data, while such data can be very expensive and infeasible.…”

“…Compared with other deep learning methods [23][24][25][26][27][28][29][30][31][33][34][35], the major advantage is that, after transfer learning, we prune the network via Fourier analysis. More specifically, we have the following advantages:…”