Dense-Stream YOLOv8n: A Lightweight Model Method for Real-Time Crowd Monitoring in Smart Libraries

Crowd monitoring in the context of smart libraries is of great significance for resource optimization and service improvement. Addressing the challenges of insufficient accuracy and real-time performance in crowd detection under high-density and side-view scenarios in dynamic library environments, this paper proposes a crowd detection method based on an improved YOLOv8n model. First, side-view crowd videos from different time periods in the library were collected, segmented into images, and manually annotated to generate a high-quality training dataset. Then, a lightweight convolutional data augmentation module called DensityNet was designed to enhance the model's feature extraction capabilities in crowded occlusion scenes. Subsequently, model pruning and knowledge distillation techniques were combined to reduce model complexity and improve detection real-time performance, adapting it to the computational requirements of edge devices. Finally, a region detection algorithm was designed to better accommodate the needs of crowd monitoring in high-density and constrained-view dynamic environments by extending the detection trigger time, thereby providing an accurate and contactless solution for people flow monitoring in smart libraries. Experimental results show that the improved YOLOv8n model achieves an average precision (mAP@0.5) of 0.99 in high-density scenarios, close to the original model's 0.991, while achieving 0.861 in mAP@0.5:0.95, an increase of 0.014 over the model before pruning; in terms of real-time performance, the frame rate (FPS) significantly increased to 254, with computational costs reduced to 4.0 GFLOP and the number of parameters decreased to 2.04M, meeting the real-time detection needs of peak crowd environments in smart libraries. This study addresses the unique people flow monitoring requirements of smart libraries by proposing an efficient and accurate solution, which not only optimizes resource and service management but also provides new technical support for the intelligent management of other domains.