# An Improved Lightweight YOLOv8 Network for Early Small Flame Target Detection
## 5 Analysis of Results
### Ablation Experiment
These results indicate that Improvement 4 is more effective for the accuracy of small target objects, more capable of capturing the spatial information and distribution characteristics of objects, and more robust for the detection of smaller objects. It can be demonstrated that Improvement 4 has a more pronounced impact on the enhancement of accuracy than the optimization of single-picture detection time and the expansion of the model size. Improvement 5 entails the advancement of both Improvement 2 and Improvement 3 in the original YOLOv8 network, resulting in an improvement in accuracy of 1%. Improvement 8 represents an 8% improvement in accuracy compared to the original YOLOv8n while also improving the speed of single-picture detection by 0.9 ms. However, this comes at the cost of an increased model size of 0.8 MB. Improvement 6 represents a 1% improvement in accuracy for both Improvement 1 and Improvement 3, which is added to the network. This results in an improvement of 1.7% in accuracy, a decrease in model size of 0.8 MB, and a decrease in detection speed. Improvement 7 is the improved network used in this paper. It adopts the BiFPN model architecture, adds the EMA mechanism and the NWD loss function, reduces part of the redundant computation, increases the number of a few parameters, and benefits from the bidirectional mutual fusion of different features to effectively increase the effective information, thereby making the model features richer. In comparison to the initial YOLOv8 network, it is possible to enhance the accuracy by a further 2.7% while simultaneously reducing the number of parameters by 22.4%. The model is capable of maintaining and enhancing target detection accuracy while also necessitating a more lightweight configuration, one that is more readily deployable on embedded devices at the edge. To further evaluate the detection performance of the proposed improved model, a comparative analysis was conducted with the current mainstream target detection models, namely YOLOv3-tiny [24], YOLOv4-tiny [25], YOLOv5s, YOLOv7-tiny [26], YOLOv8, and the improved YOLOv8 detection model proposed in this paper. All models were evaluated under identical conditions to ensure a fair and objective comparison. The experimental data are shown in Table 4.  
The results in the table show that the improved YOLOv8 algorithm in this paper is superior to the original YOLOv8n model in terms of detection accuracy, detection speed, and model size. Compared with the current mainstream lightweight models, YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, and YOLOv7-tiny, in terms of parameter quantity, the model size of this paper is reduced by 41.4%, 54.7%, 15%, and 29.4%, respectively, compared to the above four models. The mAP0.5 data are improved by 21.7%, 19.9%, 7.9%, and 2.7%, respectively, compared to the other four networks. Although the FPS of the model in this paper is slightly lower than the other four, the mAP0.5 still shows a significant improvement when the parameter calculation amount is greatly reduced, which fully shows that the large parameter quantity cannot guarantee the high accuracy of the network model. The improved YOLOv8 algorithm in this paper not only realizes model lightweighting, but is also more convenient for deployment on edge embedded devices and effectively improves the detection accuracy of the model, which can effectively meet the requirements of lightweightness, real-time tracking, and accuracy for the detection model for early fire small target detection.