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<p>Mobility robots for elderly care not only satisfy the basic needs of disabled seniors but also help ensure their safety. Safety monitoring is particularly critical when disabled seniors remain alone indoors. This research focuses on detecting flame and smoke targets in indoor environments, enabling faster decision-making during fires, facilitating timely evacuation for disabled seniors, and thereby providing improved protection. This study aims to enhance detection accuracy and algorithm performance by introducing the improved YOLO11-BSCS model. The Biformer two-layer routing attention mechanism is incorporated into the Backbone and Neck of YOLO11s, replacing the original C2SPA module with C2SPA_Biformer to enable dynamic, query-aware sparse attention, reduce the number of model parameters, and improve the detection of dynamic targets. The SCConv convolution replaces the C3k2 convolution module in the original model with the C3k2_SCConv module, reducing spatial and channel redundancy during the fusion of image features extracted by the model and increasing detection speed. The loss function of the model was optimized by replacing CIoU-Loss with the SIoU-Loss module. This modification improves both convergence speed and detection accuracy. Through 600 rounds of experimental testing on 5,000 data samples, supplemented by three independent training runs using random seeds (107,325,592) for evaluation, YOLO11-BSCS achieved 94.612% accuracy, 89.678% recall, and 90.319% average precision—representing improvements of 4.934, 7.452, and 5.184%, respectively, over YOLO11s. Comparative analysis with widely used models indicates that YOLO11-BSCS provides strong generalizability, precise localization, robust detection, and overall superior performance. The necessity of each model enhancement was validated through ablation experiments, confirming that all modifications contributed meaningfully to performance improvements. These findings provide a valuable reference for addressing similar challenges in object detection.</p>

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YOLO11-BSCS: an enhanced attention-optimized framework for real-time indoor flame and smoke detection in elderly care mobile robots

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月25日

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  • Yao Wang,
  • Yanzhen Wang,
  • Zhimin Wei,
  • Jianyong Li,
  • Xiaolong Zhou,
  • Haoyu Zhang,
  • Linlin Cao

摘要

Mobility robots for elderly care not only satisfy the basic needs of disabled seniors but also help ensure their safety. Safety monitoring is particularly critical when disabled seniors remain alone indoors. This research focuses on detecting flame and smoke targets in indoor environments, enabling faster decision-making during fires, facilitating timely evacuation for disabled seniors, and thereby providing improved protection. This study aims to enhance detection accuracy and algorithm performance by introducing the improved YOLO11-BSCS model. The Biformer two-layer routing attention mechanism is incorporated into the Backbone and Neck of YOLO11s, replacing the original C2SPA module with C2SPA_Biformer to enable dynamic, query-aware sparse attention, reduce the number of model parameters, and improve the detection of dynamic targets. The SCConv convolution replaces the C3k2 convolution module in the original model with the C3k2_SCConv module, reducing spatial and channel redundancy during the fusion of image features extracted by the model and increasing detection speed. The loss function of the model was optimized by replacing CIoU-Loss with the SIoU-Loss module. This modification improves both convergence speed and detection accuracy. Through 600 rounds of experimental testing on 5,000 data samples, supplemented by three independent training runs using random seeds (107,325,592) for evaluation, YOLO11-BSCS achieved 94.612% accuracy, 89.678% recall, and 90.319% average precision—representing improvements of 4.934, 7.452, and 5.184%, respectively, over YOLO11s. Comparative analysis with widely used models indicates that YOLO11-BSCS provides strong generalizability, precise localization, robust detection, and overall superior performance. The necessity of each model enhancement was validated through ablation experiments, confirming that all modifications contributed meaningfully to performance improvements. These findings provide a valuable reference for addressing similar challenges in object detection.