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<p>Tobacco warehousing requires continuous surveillance to mitigate risks like unauthorized access, fire hazards, and moisture-induced decay. To address these challenges, this paper proposes an edge-cloud collaborative surveillance framework with adaptive deep learning, termed YOMO-TF (YOLO + MobileOne + Transformer + Federated self-distillation). The architecture consists of three layers: edge layer- employing lightweight models (YOLOv8-nano for real-time object detection and MobileOne-S for effective image classification) for performing fast, on-device video analytics without storing data in cloud. Next, the adaptive learning layer, where a federated self-distillation mechanism enables continuous knowledge refinement over distributed devices without centralized retraining; and the cloud layer, that leverages attention-based schemes like Temporal Shift Transformer (TST) for temporal anomaly detection. This hybrid model ensures high responsiveness, reduced bandwidth usage, with enhanced privacy. Experimental evaluations demonstrate that the proposed model attains 98.6% accuracy, 99.5% precision, 97.6% recall, and 98.5% F1-score, outperforming traditional schemes in both reliability and efficiency. These outcomes highlight the proposed framework’s potential as a scalable, privacy-preserving, and real-time solution for tobacco warehouse safety management, with broad applicability to other industrial safety domains.</p>

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YOMO TF based edge cloud collaborative surveillance framework for tobacco warehouse safety management

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月26日

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  • Tianhe Song,
  • Hao Tian,
  • Xinghua Qin,
  • Lingrui Lv,
  • Wenjuan Zhou

摘要

Tobacco warehousing requires continuous surveillance to mitigate risks like unauthorized access, fire hazards, and moisture-induced decay. To address these challenges, this paper proposes an edge-cloud collaborative surveillance framework with adaptive deep learning, termed YOMO-TF (YOLO + MobileOne + Transformer + Federated self-distillation). The architecture consists of three layers: edge layer- employing lightweight models (YOLOv8-nano for real-time object detection and MobileOne-S for effective image classification) for performing fast, on-device video analytics without storing data in cloud. Next, the adaptive learning layer, where a federated self-distillation mechanism enables continuous knowledge refinement over distributed devices without centralized retraining; and the cloud layer, that leverages attention-based schemes like Temporal Shift Transformer (TST) for temporal anomaly detection. This hybrid model ensures high responsiveness, reduced bandwidth usage, with enhanced privacy. Experimental evaluations demonstrate that the proposed model attains 98.6% accuracy, 99.5% precision, 97.6% recall, and 98.5% F1-score, outperforming traditional schemes in both reliability and efficiency. These outcomes highlight the proposed framework’s potential as a scalable, privacy-preserving, and real-time solution for tobacco warehouse safety management, with broad applicability to other industrial safety domains.