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<p>To address the challenges of low detection accuracy and efficiency in YOLOv7 for indoor object detection, this paper proposes a novel algorithm, SBP-YOLOv7, tailored for complex indoor environments. The improvements include the introduction of the parameter-free SimAM attention mechanism into the backbone network to enhance the focus on target objects, the design of a Short-BiFPN (lightweight weighted bidirectional feature pyramid network) for efficient multi-scale feature fusion in the neck network, the integration of PConv (partial convolution) in the ELAN module of the backbone for lightweight design, and the adoption of GSConv (Group Shuffle Convolution) and VoVGSCSP (Volumetric Grid Spatial Cross Stage Partial) modules in the neck to further reduce computational complexity. Experiments were conducted on the indoor object subsets extracted from the PASCAL VOC and COCO datasets, as well as a custom indoor object dataset. The results show that, compared to YOLOv7, SBP-YOLOv7 reduces the number of parameters by 32.00, 28.23, and 32.00%, respectively, while improving detection accuracy by 3.94, 3.50, and 3.84%. Additionally, SBP-YOLOv7 outperforms mainstream algorithms such as Faster R-CNN, SSD, and YOLOv5l.</p>

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Indoor object detection algorithm based on SBP-YOLOv7

  • 期刊论文

  • 发表日期：

    2025年8月30日

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  • Huiyan Han,
  • Wanning Li,
  • Xie Han,
  • Liqun Kuang,
  • Xiaowen Yang

摘要

To address the challenges of low detection accuracy and efficiency in YOLOv7 for indoor object detection, this paper proposes a novel algorithm, SBP-YOLOv7, tailored for complex indoor environments. The improvements include the introduction of the parameter-free SimAM attention mechanism into the backbone network to enhance the focus on target objects, the design of a Short-BiFPN (lightweight weighted bidirectional feature pyramid network) for efficient multi-scale feature fusion in the neck network, the integration of PConv (partial convolution) in the ELAN module of the backbone for lightweight design, and the adoption of GSConv (Group Shuffle Convolution) and VoVGSCSP (Volumetric Grid Spatial Cross Stage Partial) modules in the neck to further reduce computational complexity. Experiments were conducted on the indoor object subsets extracted from the PASCAL VOC and COCO datasets, as well as a custom indoor object dataset. The results show that, compared to YOLOv7, SBP-YOLOv7 reduces the number of parameters by 32.00, 28.23, and 32.00%, respectively, while improving detection accuracy by 3.94, 3.50, and 3.84%. Additionally, SBP-YOLOv7 outperforms mainstream algorithms such as Faster R-CNN, SSD, and YOLOv5l.