Hazards caused by fire are a serious threat to human life and property. According to data from the Global Disaster Database, from 2013 to 2022 the average number of deaths and missing persons due to forest and grassland fires alone reached 904,000 people. Data released by Global Forest Watch (GFW) and the World Resources Institute (WRI) indicate that on a global scale the forest area destroyed by wildfires is now double what it was at the beginning of this century. According to satellite data, the annual forest area destroyed by wildfires has increased by approximately 3 million hectares compared to the year 2001. In addition to forest fires, the increasing impact of other types of fires, such as electrical fires, is becoming more severe as society continues to progress and develop. The frequency of these incidents is on the rise. Real-time monitoring of fire-prone areas, timely fire alarms, and rapid localization of fire incidents are of paramount importance for safeguarding human life, property, and industrial safety.

Traditional fire detection methods primarily involve contact-based fire detectors, such as carbon monoxide sensors, temperature sensors, smoke detectors, etc. Rachman, F. et al. [1] proposed a fuzzy logic-based early fire detection system using KY-026 (fire detection), MQ-9 (smoke detection), and DS18b20 (temperature detection) sensors. Huang Ye et al. [2] proposed a wireless fire detection node design method based on multi-source sensor data fusion and provided a complete hardware selection and software data fusion processing method. Solorzano Soria, A.M. et al. [3] proposed a gas sensor-based array to speed up fire alarm response. Li Yafei et al. [4] developed a mid-infrared carbon monoxide (CO) and carbon dioxide (CO22) dual gas sensor system for early fire detection. Liu Xiaojiang et al. [5] proposed a sensor optimization strategy and an intelligent fire detection method based on the combination of particle swarm optimization algorithm. Although contact fire detectors are commonly used in various public scenes, their detection range is limited to small indoor spaces, and it is difficult to apply them to large indoor spaces and outdoor open spaces where open flames are strictly prohibited. Moreover, traditional contact fire detectors are prone to age-related failures and require a lot of manpower and resources for maintenance and management.

Compared to contact fire detection using sensors, non-contact video fire detection technology has the advantages of no additional hardware, intuitive and comprehensive fire information, and large detection range. While real-time monitoring of fire appears to be a binary classification problem for images, in practice it requires further detection of fire images based on the classification. The use of fire image detection is justified due to the extensive coverage of video surveillance systems. Early fire incidents are often challenging to detect, and sometimes fires can escalate to an uncontrollable stage within a short timeframe. Therefore, relying solely on image classification is insufficient for accurately pinpointing the actual location of a fire. This limitation could undoubtedly hinder the timely response and effective management of fire incidents. Traditional fire target detection algorithms include region selection, feature extraction, and classifier design. Qiu, T. et al. [6] proposed an adaptive canny edge detection algorithm for fire image processing. Ji-neng, O. et al. [7] proposed an early flame detection method based on edge gradient features. Khalil, A. et al. [8] proposed a fire detection method based on multi-color space and background modeling. However, in traditional fire target detection algorithms, manually designed features lack strong generalization and exhibit limited robustness. The emergence of CNNs has gradually replaced traditional handcrafted feature methods, offering superior generalization and robustness compared to traditional fire detection approaches. Majid, S. et al. [9] proposed an attention-based CNN model for the detection and localization of fires. Chen, G. et al. [10] proposed a lightweight model for forest fire smoke detection based on YOLOv7. Dogan, S. et al. [11] proposed an automated accurate fire detection system using ensemble pretrained residual network. In recent years, a new generation of transformer-based deep learning network architectures has gradually started to shine. Li, A. et al. [12] proposed a combination of BiFPN and Swin transformer for the detection of smoke from forest fires. Huang, J. et al. [13] proposed a small target smoke detection method based on a deformable transformer. Although these transformer-based network architectures have achieved good results in fire detection, they tend to be more complex (i.e., the number of parameters reaches about 20 M or 30 M) and not very lightweight. In order to ensure a lightweight transformer architecture-based model, scholars have started to research solutions such as EfficientViT [14], EfficientFormerV2 [15], MobileViT [16], etc., although these network models have not become widely used in fire detection to date.

2. MobileViT

3. 预测头