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<p>To address the challenges of high passenger density, severe object occlusion, and limited computational resources in escalator scenarios, this paper proposes SAL-YOLO, a lightweight detection model, and develops a safety warning system. The model is optimized in multiple dimensions based on the YOLOv8n architecture: (1) A lightweight backbone network, StarNet, is designed by integrating depth-wise separable convolutions and residual connections to improve feature extraction efficiency. (2) A Transformer-based Adaptive Image Feature Integration (AIFI) module is integrated to capture global contextual dependencies through a self-attention mechanism. (3) The C2f_star feature fusion module is designed to enhance the discriminative power of local features in scenarios with occlusion and small objects. (4) A Lightweight Shared Convolution (LWSC) detection head is proposed that uses shared convolution and a dynamic stride mechanism to optimize computational load. Experiments on a custom-built dataset show that SAL-YOLO achieves 92.6% Precision (P), 95.4% mAP@50, and 79.9% mAP@(0.5:0.95). The computational complexity and parameter count are reduced by 47.1% and 50%, respectively, compared to the baseline model YOLOv8n. Furthermore, by introducing a knowledge distillation framework with a Similarity-Preserving (SP) feature distillation strategy, the model’s performance improved to 94.6% P and 80.6% mAP@(0.5:0.95), achieving an inference speed of 500 FPS. Building on the aforementioned model, an innovative safety warning system is developed by integrating the DeepSeek large language model, establishing an end-to-end mechanism of ’behavior recognition - risk assessment - response plan generation’. When abnormal behaviors, such as falls or bending over, are detected, the system dynamically generates graded response strategies and triggers multi-modal alerts. Empirical validation shows that the system is highly robust in complex scenarios, such as varying illumination and dense occlusions, offering both a theoretical methodology and an application paradigm for intelligent public safety surveillance.</p>

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SAL-YOLO-DeepSeek: a lightweight real-time detection and LLM-driven decision framework for intelligent escalator safety monitoring

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月18日

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  • Qibing Wang,
  • Dongyang Wang,
  • Jiawei Lu,
  • Gang Xiao,
  • Dongming Liang,
  • Guoxiong Lu,
  • Haibo Shao

摘要

To address the challenges of high passenger density, severe object occlusion, and limited computational resources in escalator scenarios, this paper proposes SAL-YOLO, a lightweight detection model, and develops a safety warning system. The model is optimized in multiple dimensions based on the YOLOv8n architecture: (1) A lightweight backbone network, StarNet, is designed by integrating depth-wise separable convolutions and residual connections to improve feature extraction efficiency. (2) A Transformer-based Adaptive Image Feature Integration (AIFI) module is integrated to capture global contextual dependencies through a self-attention mechanism. (3) The C2f_star feature fusion module is designed to enhance the discriminative power of local features in scenarios with occlusion and small objects. (4) A Lightweight Shared Convolution (LWSC) detection head is proposed that uses shared convolution and a dynamic stride mechanism to optimize computational load. Experiments on a custom-built dataset show that SAL-YOLO achieves 92.6% Precision (P), 95.4% mAP@50, and 79.9% mAP@(0.5:0.95). The computational complexity and parameter count are reduced by 47.1% and 50%, respectively, compared to the baseline model YOLOv8n. Furthermore, by introducing a knowledge distillation framework with a Similarity-Preserving (SP) feature distillation strategy, the model’s performance improved to 94.6% P and 80.6% mAP@(0.5:0.95), achieving an inference speed of 500 FPS. Building on the aforementioned model, an innovative safety warning system is developed by integrating the DeepSeek large language model, establishing an end-to-end mechanism of ’behavior recognition - risk assessment - response plan generation’. When abnormal behaviors, such as falls or bending over, are detected, the system dynamically generates graded response strategies and triggers multi-modal alerts. Empirical validation shows that the system is highly robust in complex scenarios, such as varying illumination and dense occlusions, offering both a theoretical methodology and an application paradigm for intelligent public safety surveillance.