工业场景中的服装规范监控方法基于改进的YOLOv8n和DeepSORT.
Jiadong Zou1, Tao Song1, Songxiao Cao1
1College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
这项研究引入了一种增强的YOLOv8n物体检测模型,具有改进的功能融合和注意力机制,用于准确的服装规范监控. 该方法有效地减少了错误报警和错过的检测,特别是对于小物品,如帽子和口罩.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度学习对象检测对于服装规范监控至关重要,但与小目标作斗争,导致错误报警和错过检测.
- 现有的最先进的模型需要改进,以处理现实世界服装规范执行的细微差别,特别是关于配件的细微差别.
研究的目的:
- 提出一种新且改进的深度学习方法,用于准确监测着装规范.
- 为了增强对象检测能力,用于像帽子和口罩这样的小目标.
- 为了减少虚假警报和错过检测在现实世界服装规范执法场景.
主要方法:
- 一个改进的YOLOv8n模型,采用了新的FPN-PAN-FPN (FPF) 部结构,用于功能融合.
- 接收场注意力卷积操作 (RFAConv) 和集中线性注意力 (FLatten) 机制的整合,以增强特征提取和接收场扩展.
- 实施DeepSORT跟踪,以获得多实例信息,并为现实场景监控采用新的服装规范判断标准.
主要成果:
- 改进的YOLOv8n模型显示了平均精度 (mAP) 的提高,同时减少了模型大小.
- 综合系统在现实世界中有效地识别了违反着装规范的情况.
- 与基线模型相比,拟议的方法显著减少了虚假报警和错过检测.
结论:
- 这种新的方法为自动化服装规范监控提供了一个强大的解决方案,提高了准确性和可靠性.
- 对YOLOv8n模型的改进和新的判断标准解决了检测小物体的关键局限性.
- 这种方法为需要精确对象检测和分类的现实应用提供了实用和有效的工具.
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