## 4 Conclusions  
In this paper, we propose the SDCB-YOLO object detection algorithm, built upon YOLOv8n, specifically for the detection of safety helmets and reflective clothing. YOLOv8 often encounters challenges in tasks involving crowded scenes, complex environments, blurry images, and small object detection. Consequently, we first introduce the SE attention mechanism to adjust resource allocation, focusing more computational resources on more significant pixels, thereby addressing the issue of poor detection performance. We revise the loss function to reduce computational complexity and incorporate CARAFE \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**YOLOv8** & **SE** & **DIOU** & **CARAFE** & **BiFPN** & **Precision (\%)** & **Recall (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** \\ \hline \(\surd\) & & & & & 93.0 & 87.3 & 93.6 & 70.4 \\ \(\surd\) & \(\surd\) & & & & 92.9 & 88.8 & 94.1 & 70.7 \\ \(\surd\) & \(\surd\) & \(\surd\) & & & 91.9 & 90.1 & 94.5 & 69.6 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & 93.5 & 93.4 & 96.7 & 77.0 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 95.1 & 92.7 & 97.1 & 78.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment table. Figure 7: Normalized confusion matrix. for upsampling operations, which enlarges the receptive field and yields feature maps with richer information, leading to more precise feature extraction and a reduced loss of feature information. Furthermore, we substitute the original PAN with the BiFPN to perform weighted calculations on different feature maps. Experimental results showcase that the SDCB-YOLO model exhibits higher accuracy and practicality compared to most other models, rendering it more suitable for real-world applications in safety helmet and reflective clothing detection. While the algorithm presented in this article represents a significant advancement in enhancing detection accuracy for safety helmets and reflective clothing, it is acknowledged that the solution does have some limitations and areas for further improvement. Primarily, the focus of this article leans heavily towards optimizing accuracy, with a less pronounced emphasis on achieving a lightweight design. This aspect becomes particularly noteworthy when considering the increasing demand for efficient and resource-conscious solutions in diverse application scenarios. Furthermore, the adaptability of the proposed algorithm to more complex and challenging environments remains an area of concern. The harsh and dynamic conditions often encountered in real-world deployments pose additional difficulties that may not be fully addressed by the current approach. These environments can involve extreme lighting conditions, heavy occlusions, and dynamic changes in the visual scene, all of which can compromise detection performance. To address these shortcomings, future research should strive to balance the optimization efforts between accuracy enhancement and lightweight design, ensuring that the algorithm remains practical and efficient for deployment in resource-constrained environments. Additionally, exploring advanced techniques to improve the algorithm's robustness and adaptability to a broader range of complex scenarios would significantly expand its applicability and effectiveness. Future research will focus on advancing the real-time detection capabilities of SDCB-YOLO and enhancing safe construction practices. Although we have addressed detection issues in various complex environments, detection in special weather conditions such as at night or during heavy rain remains a challenge. Conceptualization, J.W. ; methodology, J.W. ; software, X.Y. ; validation, J.W. and X.Y. ; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, M.D. ; funding acquisition, M.D.