## 1 Introduction  
Fires in chemical plants and large-scale workshops may lead to serious property loss, equipment damage, and even casualties [1]. Half of the 20 largest industrial accidents in the world have been related to fires since 1988, which have resulted in significant economic losses [2]. The results indicate that the expansion of fire losses results from the inadequacy of effective fire detection technologies [3]. With the emergence and development of smart factories, it is important to design an effective fire detection system for providing timely early warnings when flames appear. Traditional fire detection devices generally consist of physical detectors such as smoke detectors, heat detectors, and ultraviolet flame detectors [4, 5]. To achieve the fire detection function, these detectors can only generate signals through transformations based on factors like temperature and brightness because they are unable to extract flame characteristics [6]. Therefore, the real-time accuracy of these traditional detectors is very poor [7, 8]. In order to overcome the above limitations of detection technology, it is very important to further develop fire detection technology. In recent years, visual fire detection technology has become a research hotspot because of its advantages of low cost and high accuracy. At the beginning, fire detection was done using artificially extracted features that included colour, area, and simple texture [9, 10]. [PERSON] and [PERSON] [11] established a colour model of flame in HIS space and used differential methods to obtain suspected flame regions, thereby building feature extractors. [PERSON] et al. [12] judged fires by area growth rate and centre-of-mass stability. A real-timefire detection method based on the hidden Markov model (HMM) analysing fire characteristics was proposed by [PERSON] et al. in [13]. [PERSON] et al. [14] used high-frequency features of a flame to describe the flame after analysing its flickering properties by applying a wavelet transform to the boundary of the flame. These researchers have improved the accuracy of fire detection by making feature extractors. However, because of the complexity and uncertainty of fire types in practical applications, algorithms extracting simple features struggle to distinguish between fires and fire-like, which leads to low robustness [15]. Detection methods that incorporate deep learning have become mainstream due to their ability to efficiently and automatically learn and extract image features. Researchers have developed two types of deep learning target detection methods, i.e., region-based and regression-based methods [16], which have achieved outstanding results in areas such as disaster prediction and medical image recognition [17; 18; 19]. To achieve better detection performance, some scholars have proposed a region-based convolutional neural network (CNN) approach in the field of fire detection. [PERSON] et al. [20] proposed an efficient CNN fire detection system for video surveillance applications. [PERSON] et al. [21] employed existing models based on Faster Region-CNN (R-CNN) and Single Shot Multi-Box Detector (SSD) MobileNet to develop an indoor fire and smoke detection system. [PERSON] et al. [22] proposed a convolutional neural network (R-CNN) framework based on the Dirichlet Process Gaussian Mixture Model (DPGMM), which is used to detect fire autonomously in complex environments. [PERSON] et al. [23] proposed a fire warning system combining 3D-CNN and Bayesian classifiers to estimate the possibility of wildfires. However, it is difficult to achieve real-time target detection for CNN-based methods due their high computational complexities. [PERSON] et al. [24] first proposed the regression-based method called You Only Look Once (YOLO), which takes the whole graph as input to the network. Then, the position and category of the prediction box at the output layer can be obtained simultaneously.