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食品冷柜仓库的轻量级物体检测模型
Jiayu Yang1, Zhihong Liang2, Mingming Qin1
1Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming, China.
Scientific reports
|January 20, 2025
概括
本研究介绍了YOLOv8-RSS模型用于智能食品冷柜仓库管理. 它通过在具有挑战性的低温环境中准确检测人员和叉车来提高安全性和效率.
科学领域:
- 物流和供应链管理的物流和供应链管理.
- 人工智能在仓储中的应用
- 计算机视觉用于工业安全
背景情况:
- 食品冷仓库至关重要,但面临着高密度存储和极端温度的挑战.
- 传统的管理方法不足以提高效率和减轻这些环境中的安全风险.
- 增强仓库运营需要智能和数字化解决方案.
研究的目的:
- 为食品冷仓库应用提出一种新,轻量级,高精度的物体检测模型 (YOLOv8-RSS).
- 提高食品冷仓库内的运营效率和安全性.
- 为了在复杂的仓库环境中准确和快速检测人员和叉车.
主要方法:
- 开发了YOLOv8-RSS模型,结合了新的C2f_RDB模块,以提高准确性和减少计算负载.
- 将SimAM注意力机制应用于脊柱的最后一层,以便在没有参数增加的情况下进行集中图像分析.
- 软非最大抑制 (Soft-NMS) 的实施,以提高检测精度,而不是传统的NMS.
主要成果:
- 在YOLOv8-RSS模型中,参数数量减少了0.05M,FLOP减少了0.8G.
- 检测准确度有所提高,mAP@0.5增加了1.4%,mAP@0.5:0.95增加了3.9%.
- 该模型证明了在食品冷库仓库数据集中有效检测人员和叉车.
结论:
- YOLOv8-RSS模型为食品冷仓库中的物体检测提供了一个轻量级和高精度的解决方案.
- 它为解决复杂的检测需求和提高运营安全和效率提供了强大的技术支持.
- 该模型具有重要的应用价值,用于现代化食品冷器仓库管理系统.
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