# Occlusion-Aware Worker Detection in Masonry Work: Performance Evaluation of YOLOv8 and SAMURAI
## 4 Discussion
### Future Research  
To further enhance the effectiveness of worker detection systems in construction environments, several future research directions are proposed. One potential area of future study is the proposed development of hybrid models that leverage the strengths of both YOLOv8 and SAMURAI. By combining YOLOv8's fast detection speed with SAMURAI's robust occlusion handling, a more efficient and accurate detection framework could be achieved. For instance, a conceptual two-stage detection process could be implemented, where YOLOv8 serves as the primary detection system, followed by SAMURAI applying additional occlusion correction techniques. The illustrative workflow of this conceptual hybrid detection framework is shown in Figure 9.  
This figure hypothetically illustrates the two-stage detection process that integrates YOLOv8 and SAMURAI, highlighting the role of each model and the expected detection outcome. The first stage employs YOLOv8, which excels in fast real-time object detection. YOLOv8 is applied as the primary detection model, quickly identifying workers under non-occlusion or low-occlusion conditions. If YOLOv8 successfully detects a worker, the result is directly output as the final detection. However, in cases where occlusion is present, YOLOv8 often fails to maintain detection due to its frame-based processing.  
In this conceptual framework, YOLOv8's detection failure is inferred through predefined conditions such as low detection confidence or temporal discontinuity (e.g., missing detection in sequential frames). When these conditions are met, the system assumes potential occlusion and activates SAMURAI for secondary tracking.  
To address this issue, the second stage introduces SAMURAI, which incorporates motion-aware instance-level memory to enhance detection under high-occlusion conditions. When YOLOv8 fails to detect a worker due to severe occlusion, SAMURAI is activated as a secondary detection model, leveraging its tracking mechanism to re-identify the worker. By maintaining continuous traceability across frames, SAMURAI can recover detection failures caused by occlusion and provide more stable worker tracking in construction environments.  
This proposed hybrid framework is designed to balance the trade-off between speed and accuracy by prioritizing YOLOv8 for rapid detection in clear conditions while leveraging SAMURAI for occlusion correction in challenging scenarios. The integration of both models minimizes the computational burden associated with using SAMURAI for the entire detection process while ensuring reliable worker tracking under varying occlusion levels.  
Figure 9: Conceptual hybrid detection framework: integration of YOLOv8 and SAMURAI.  
In addition, future research should consider benchmarking SAMURAI against other state-of-the-art tracking algorithms, such as transformer-based trackers and memory-augmented networks. Comparative evaluations with these advanced models would provide deeper insights into SAMURAI's unique strengths and limitations, allowing for a more comprehensive understanding of its potential applications across diverse tracking scenarios.  
Another crucial research direction involves evaluating performance in diverse environmental conditions. While this study was conducted in a controlled setting, future experiments should consider varied lighting conditions and complex backgrounds to assess the adaptability of YOLOv8 and SAMURAI. Additionally, testing in environments with more intricate occlusion patterns could provide further insights into SAMURAI's robustness and limitations.  
Overall, future studies should focus on enhancing hybrid detection strategies and testing in varied real-world conditions to develop more reliable and scalable worker detection models for construction environments.