## 6 Conclusions  
In response to the challenges in identifying unsafe behaviors of construction workers, this paper proposed an identification method based on the improved YOLOv5s model. First, the study analyzed the existing issues in the identification of unsafe behaviors and the limitations of the original model. Next, the traditional YOLOv5s model was enhanced in three key areas, and a dataset was collected, processed, and augmented accordingly. Finally, the improved model was validated through experimental verification. Compared to traditional methods, the approach presented in this paper significantly enhances the accuracy of detecting unsafe behaviors across various construction scenarios. It reduces the incidence of both missed and false detections, while preserving the speed and efficiency of the original model, ensuring real-time detection performance. Additionally, the improved model demonstrates strong generalization capability and robustness, making it well suited for practical engineering applications. The method proposed in this paper focuses on identifying unsafe behaviors of construction workers who fail to wear safety protection equipment. Future research can expand to include the identification of other unsafe behaviors. In addition to using target detection algorithms, human posture estimation techniques, such as the OpenPose algorithm, can be used to assess the status of construction workers. While the improvements introduced in this paper significantly enhance model performance, they may also result in a slight decrease in detection speed. To address this, future work could explore the adoption of a more streamlined network architecture to reduce computational load and model complexity. Building upon the current framework, next-generation safety systems could synergize the enhanced detection model with IoT-enabled edge devices--embedding real-time inference modules in smart helmets equipped with inertial measurement units (IMUs) and RFID-enabled PPE, while integrating environmental LiDAR/Gas sensors for comprehensive hazard awareness. This distributed architecture would enable multi-modal data fusion, where vision-based behavior recognition complements sensor-derived physiological metrics (e.g., fatigue levels from heart rate variability) and proximity alerts from Bluetooth beacons. Concurrently, expanding to full-body risk assessment requires hybrid architectures combining the current detection backbone with spatiotemporal graph convolutional networks for skeleton-based action recognition, capable of identifying ergonomic hazards like improper lifting postures and scaffold overreach. To address computational constraints, federated learning schemes could distribute model updates across site-specific edge nodes, while maintaining central compliance standards, complemented by neural architecture search (NAS) techniques to evolve leaner submodels (<8 MB) for wearable deployments. The envisioned ecosystem would not only detect PPE non-compliance but also proactively mitigate risks through AR-assisted worker guidance systems and automated equipment lockdown protocols when imminent dangers are predicted, ultimately bridging the gap between reactive monitoring and preventive safety culture in global construction practices. **Author Contributions:** Conceptualization, methodology, writing--reviewing and editing, P.W. ; data curation, writing--original draft preparation, visualization, H.L. ; supervision, Y.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to project confidentiality regulations. **Conflicts of Interest:** Author [PERSON] was employed by the company Nanjing Narnui Water Conservancy and Hydropower Technology Co., Ltd.