# LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection
## 5 Conclusions  
Aiming at the problem of rockfall detection on highway slopes, this paper introduces a new LHB-YOLOv8 model based on YOLOv8. Compared with the original model, the model overcomes the adverse effects of falling rocks of different sizes, shapes, and colors, which are often in motion, and also meets the deployment requirements of embedded devices. Furthermore, it significantly improves the accuracy of rockfall detection on high-speed slopes. First, the LSKAttention module is introduced in the backbone part to enable the  
Figure 9: Comparison of LBH-YOLOv8 with baseline model assays. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **P (\%)** & **R (\%)** & **[EMAIL_ADDRESS] (\%)** & **F1 Score (\%)** & **Parameters (M)** \\ \hline RT-DETR2 & 71.1 & 67.7 & 61.9 & 60.0 & 3.2 \\ RT-DETR3 & 89.2 & 84.1 & 83.8 & 83.0 & 3.2 \\ YOLOv5 & **90.7** & 82.2 & 83.3 & 86.0 & **1.8** \\ YOLOv7 & 77.4 & 79.9 & 75.8 & 81.0 & 6.0 \\ YOLOv9 & 83.3 & 74.3 & 80.0 & 79.0 & 2.6 \\ YOLOv10 & 87.3 & 83.6 & 83.0 & 86.0 & 2.7 \\ Ours & 90.3 & **84.2** & **89.3** & **87.0** & 2.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of the LHB-YOLOv8 model with other models. model to capture information on various types of falling rocks, which effectively retains the global features related to falling rocks and enhances the detection accuracy of the model. Subsequently, the BiFPN structure is used to reconstruct the neck structure of the original model, which effectively reduces the number of model parameters and computation and decreases the model's size to enhance its feature fusion capability. Finally, the additional detection head enriches the efficiency of multi-scale rockfall detection. The experimental results show that the LHB-YOLOv8 model outperforms existing detection methods in terms of real-time performance and detection accuracy. In addition, our model has fewer parameters, and after quantizing and accelerating it to improve the detection speed, it is capable of efficient deployment and application in real high-count scenarios. In future work, the rich promise of targeting slope decline detection can be further explored and refined. The potential to fine tune the model for dataset extension by using hyperparameters, alternative architectures, and integration techniques to optimize the model's performance is another avenue for improvement, as a more diverse and wider range of datasets can enhance the model's robustness and generality. Secondly, in complex terrain such as mountain slopes, adaptation to changing weather conditions, especially haze, is still an unsolved problem that significantly affects the effectiveness and accuracy of image-based target detection techniques. Haze seriously interferes with the performance of surveillance systems by blurring images, reducing contrast, and obscuring distant targets. Such unfavorable weather conditions result in the loss of image details, making it difficult for conventional image processing and target detection algorithms to extract key features, which, in turn, affects the accuracy of real-time monitoring. Therefore, looking ahead, we will investigate how to enhance target detection capabilities and embedded integration in such adverse environments. Conceptualization, A.Y. and H.F.; methodology, A.Y. ; software, H.F.; validation, A.Y., H.F. and Y.X. ; formal analysis, A.Y. ; investigation, Y.X. ; resources, L.W. ; data curation, H.F.; writing--original draft preparation, A.Y. and H.F.; writing--review and editing, A.Y., H.F., Y.X., L.W. and J.S. ; supervision, Y.X. ; project administration, Y.X. ; funding acquisition, A.Y. All authors have read and agreed to the published version of the manuscript. This work was financially supported by the National Natural Science Foundation of China under research project number 61873249. Not applicable. The raw data supporting the conclusions of this article will be made available by the authors upon request. Author [PERSON] was employed by the company Jiangxi Ganyue Expressway Co., Ltd.