A Real‐Time Automated Library Inventory System Based on Edge‐Cloud Collaboration

Library inventory is vital for collection management and reader satisfaction. Conventional manual methods cannot support real‐time updates, while existing automated solutions relying on centralized cloud computing suffer from bandwidth and latency limitations. To address these issues, we propose an edge‐cloud collaborative real‐time book inventory system. Spine detection and text recognition are executed on embedded edge devices, while the cloud handles rapid data retrieval to balance timeliness and accuracy. We design lightweight models for edge deployment, including the Library You Only Look Once (Lib‐YOLO) detector with a StarNet backbone, shared convolutional head, and dual‐scale hierarchical detection, supporting rotated objects for robust spine extraction. The optimized paddle practical optical character recognition (PP‐OCR) pipeline removes text rectification and integrates a filtering algorithm to reduce redundant computation and improve efficiency. Deployed on an NVIDIA Jetson Nano, the system achieves 73 ms spine detection latency, 191 ms text recognition latency, and 97.1% overall accuracy under simulated library conditions. The Lib‐YOLO model contains only 1.39 M parameters with 99% mean average precision (mAP), demonstrating the feasibility of precise, real‐time inventorying in resource‐constrained embedded environments.