# LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection
## 4 Results and Analysis
### Ablation Experiments  
In this study, we conducted ablation experiments based on a self-constructed high-speed slope rockfall dataset to evaluate the improvement of each module in our proposed LHB-YOLOv8 model. We used the same hyperparameters throughout the experiments to rank the improvements in the trunk, neck, and detection head sections separately, using \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attention Mechanism** & **P (\%)** & **R (\%)** & **[EMAIL_ADDRESS] (\%)** & **F1-Score (\%)** & **Parameters (M)** \\ \hline CAFM & 90.6 & 84.2 & 87.0 & 87.0 & 2.6 \\ CBAM & 90.5 & 83.8 & 85.5 & 87.0 & 2.4 \\ CPCA & 91.0 & 84.1 & 86.3 & **87.5** & 2.4 \\ SEAttention & 91.1 & 84.0 & 87.4 & 87.0 & 2.3 \\ SimAM & **91.1** & 84.0 & 87.4 & 87.0 & **2.3** \\ Ours & 90.3 & **84.2** & **89.3** & 87.0 & 2.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results for different attention modules. the original YOLOv8n as the baseline. The first of these experiments is the baseline result of YOLOv8, which was used to analyze the performance improvement effect of each module, and the second experiment denotes the addition of the LSK module to the YOLOv8 trunk section. The third experiment denotes the replacement of the YOLOv8 neck structure with the BiFPN module. The fourth is an experiment denoting the addition of an extra-small-target detection head to YOLOv8. The fifth experiment represents the addition of the LSK module to the YOLOv8 trunk section while simultaneously replacing the neck structure with a BiFPN module. The sixth experiment represents the addition of the LSK module to the YOLOv8 trunk while adding an additional small-target detection head. The seventh experiment represents the replacement of the YOLOv8 neck structure with a BiFPN module while adding an additional small-target detection head. The last experiment is our BLP-YOLOv8 model results. The experimental results of the model are shown in Table 4.  
From Table 4, it can be observed that with the introduction of the BiFPN module, the model precision is relatively increased by 2.7%, and the amount of parameters is reduced by 33.3%, which allows the information to propagate bidirectionally between the different resolution levels, facilitating the fusion of low- and high-level features. The increase in small-target detection heads results in a relative increase in precision of 4.2% and a relative increase in recall of 2.2%, indicating that the newly added detection heads effectively enhance the model's ability to detect small targets. The introduction of the LSK module alone improves the precision and parameter count by not much; even the recall decreases by 0.6%, but we can observe that he the P, R, [EMAIL_ADDRESS], F1, and parameter counts are improved when fused with the BiFPN module and the small-target detection head. The BiFPN module fused with a small-target detection layer improves the model P, R, and [EMAIL_ADDRESS] by 2.4%, 2.3%, and 2.4%, respectively, while the number of parameters is reduced by 26.7%. However, compared to the baseline model (YOLOv8n), the combination of improved modules significantly increases P, R, and [EMAIL_ADDRESS] by 4.3%, 2.2%, and 4.8%, respectively. Moreover, the amount of parameters is reduced by 20.2%. Therefore, the practicality and effectiveness of the improved model are demonstrated. As shown in Figure 9, comparing the results of rockfall detection, it can be seen that LHB-YOLOv8 is more effective than the baseline model in detecting rockfalls at the same location and detects the rockfalls that are missed by the baseline model, which effectively verifies that the model proposed in this paper has certain advantages over the baseline model.