# An Improved Lightweight YOLOv8 Network for Early Small Flame Target Detection
## 4 Yolov8 Improvements  
### Feature Fusion  
In recent years, feature fusion has been widely used in the field of computer vision to improve the performance of models. Feature fusion refers to the use of the complementarity  
Figure 2: Self-mined flame dataset. between features when given features of different attributes, fusing the advantages between features and thereby improving the performance of the model. Among them, multi-scale feature fusion is widely used in target detection. Multi-scale feature fusion can help the model better capture target information under different scales [9]. By fusing feature maps from different convolutions, the model's detection performance for targets of different scales is improved. However, when the convolutional network extracts image features, due to multiple downsampling operations, it will cause the loss of spatial information and edge contour pixels and cannot fully utilize the underlying semantic information. In order to effectively solve this problem, researchers have proposed the structure of a feature pyramid. The feature pyramid can recognize targets at different scales, and by extracting multi-scale feature information for fusion, it can improve the accuracy of the model. However, the feature pyramid requires a large amount of computation and memory, so [PERSON] et al. [10] proposed a new method for constructing a feature pyramid, namely an FPN (feature pyramid network), which can reduce the additional computation and memory consumption and can effectively solve the problem of the feature pyramid requiring a large amount of computation and memory. The original YOLOv8 network also uses the FPN structure. Specifically, the FPN adds an extra branch at the top of the network to generate high-resolution feature maps. This branch improves the resolution through upsampling and convolution operations while retaining the semantic information of the high-level feature map. After generating the high-resolution feature map, the FPN realizes feature fusion through top-down and bottom-up connections. The top-down connection fuses the high-level feature map with the low-level feature map through upsampling fusion so that the low-level feature map can obtain richer semantic information. The bottom-up connection fuses the low-level feature map with the high-level feature map through downsampling fusion so that the high-level feature map can obtain a higher resolution. The network structure of the FPN is shown in Figure 3. In the end, the FPN generates a feature pyramid with multi-scale information through the fusion of multi-layer feature maps and up- and downsampling operations. The feature pyramid can be used in target detection and semantic segmentation tasks and can pay attention to targets of different scales at the same time, improving the detection and segmentation capabilities of the network. In traditional FPNs, the feature pyramid network usually consists of feature maps of multiple levels. Each level performs top-down and bottom-up feature fusion operations to achieve the fusion and transmission of multi-scale features. Although the FPN structure can significantly reduce the amount of computation, this multi-level feature fusion will increase the amount of computation in the network, thereby affecting the speed and efficiency of  
Figure 3: FPN network structure. the network. In order to further improve the accuracy and efficiency of the model in this article, this article adopts the BiFPN (bidirectional feature pyramid network) structure [11] improved based on the FPN network structure for early small flame target detection. The new network reduces the number of layers of feature fusion, thereby improving the efficiency of the model, and adopts bidirectional feature transmission and dynamic feature fusion when the feature is transmitted downward, which can maintain high recognition accuracy. The multi-scale fusion method of the FPN is shown in Figure 4a and the multi-scale fusion method of the BiFPN is shown in Figure 4b.