# An Improved Lightweight YOLOv8 Network for Early Small Flame Target Detection
## 1 Introduction  
With the continuous development of the warehousing industry, the number of various warehouses and logistics centers is increasing and the number of warehouse-related fires is also on the rise. Once a fire occurs in such places, it generally brings incalculable losses to enterprises and serious social impacts. According to the information released by the National Fire Rescue Bureau, a total of 18,000 various factory fires were reported in 2022, with 278 casualties and a preliminary estimate of economic losses of CNY 1.5 billion. The occurrence of fires in storage places often develop from an inconspicuous spark at the beginning. When it is discovered, the fire has already formed on a large scale and it is difficult to extinguish at this time. There are often large amounts of materials piled up in the warehouse and the fire load increases [1]. Once the fire forms on a scale, it is difficult to extinguish, causing large safety accidents and huge losses. The fire alarms used in the storage environment are mostly smoke alarms and infrared detection sensors. However, these sensor alarms are not sensitive enough to detect fires in the early stages. Often, alarms only function after the fire has reached a certain degree. When the fire forms on a scale, it is extremely difficult to extinguish it. If fire warnings can be issued in the early stages of a fire with timely intervention, the fire can be pipped in the bud and the property and personnel losses caused by the fire will be minimized. In recent years, there have been significant developments in machine vision algorithms and research, particularly in target detection technology. The advancement of flame target detection technology is noteworthy, with [PERSON] et al. [2] demonstrating an effective improvement in the flame segmentation algorithm by replacing the original segmentation threshold with a proportional threshold. This approach enhances the utilization of traditional color space threshold segmentation methods while maximizing flame feature information. [PERSON] et al. [3] proposed an enhanced flame detection algorithm based on YOLOv5. The embedded coordinate attention mechanism enables the model to identify and detect the target of interest with greater precision, while a novel loss function, \(\alpha\)-IoU, is introduced to enhance the accuracy of the regression results. By integrating the model with migration learning, the accuracy of the model is improved and its ability to recognize a single flame image is accelerated. [PERSON] et al. [4] proposed an optimized hybrid kernel. An independent component analysis and echo state network flame image recognition model is proposed. To describe the feature information of the flame image in detail, a comprehensive extraction of three types of features, namely color, shape, and texture, is performed with 19 feature vectors. A hybrid kernel independent component analysis method is proposed to perform the nonlinear transformation and reduce the correlation between them. According to this method, a flame recognition model is proposed, which has good results in flame recognition performance and generalization ability. While the aforementioned research has yielded promising results in the field of flame detection algorithms, there is still a need for further advancements in the recognition and detection of small targets for early flame identification. In practical applications, the ability to detect and alert for the presence of early flames is crucial for prompt intervention and treatment. However, the current recognition and detection of small targets for early flame detection still face significant challenges. The current recognition and detection of small targets for early flames have several limitations. These include a small target pixel ratio, a lack of semantic information, and a susceptibility to interference from complex scenes. This makes it difficult to achieve accurate detection in practical applications of early flame detection, resulting in issues such as omission and incorrect detection [5].