Аннотация
This study presents a comparative analysis of the object detection models YOLOv8n and YOLOv12n in their Nano configurations for real-time construction site monitoring tasks. Key model characteristics were evaluated, including detection accuracy, recall, inference speed, and computational resource consumption across various server configurations. Particular attention was given to the use of the Slicing Aided Hyper Inference (SAHI) method to improve the detection quality of small objects, as well as the impact of scene complexity on system performance. YOLOv12n achieved significantly higher accuracy and recall than YOLOv8n. In addition, the integration of the SAHI method further enhanced small object detection performance. Parallel stream processing showed that servers equipped with Tesla L4 GPUs offered the best trade-off between inference speed and stability under multi-threaded workloads. The inclusion of data augmentation techniques simulating various weather conditions enhanced model robustness, especially for YOLOv8n, in noisy and complex scenes. The findings underscore the clear advantage of YOLOv12n for applications requiring high recall in challenging environments, while YOLOv8n demonstrated strong efficiency on resource-constrained systems. The proposed approaches can be utilized in the development of intelligent monitoring systems for construction sites, industrial facilities, and logistics hubs, including automated access control and real-time employee activity tracking.