一个基于改进的YOLOv8的手套检测算法
Shichu Li1, Huiping Huang1, Xiangyin Meng1
1Jiuli Campus, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
这项研究介绍了YOLOv8-AFPN-M-C2f,这是一个用于车间的先进手套检测算法. 它通过准确识别佩戴手套的工人来提高安全性,减少机械操作期间意外受伤.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 职业安全 在职业安全.
背景情况:
- 车间的意外伤害,包括机械损伤和烧伤,在机械操作过程中是一个重大问题.
- 确保工人佩戴适当的个人防护装备,如手套,对于预防事故至关重要.
研究的目的:
- 为车间环境开发和评估一个高效准确的手套检测算法.
- 改进现有的物体检测模型,以提高工业环境中的安全性.
主要方法:
- 提出了一个新的手套检测算法,YOLOv8-AFPN-M-C2f,基于YOLOv8架构.
- 通过用AFPN-M-C2f网络替换YOLOv8头来改善特征向量的传播和减少语义差异来创新.
- 整合了表面特征层,以增强对较小物体和表面特征的检测.
主要成果:
- 与其他网络模型相比,YOLOv8-AFPN-M-C2f模型在工厂手套检测数据集上表现出卓越的性能.
- 与基线YOLOv8.8相比,实现了50%IoU (mAP@50%) 的平均精度增加2.6%,每秒 (FPS) 增加63.8%,参数减少13%.
- 改进后的模型对较小的物体和更丰富的表面特征信息有了更好的灵敏度.
结论:
- YOLOv8-AFPN-M-C2f算法为车间设置中的实时手套检测提供了一个有效的解决方案.
- 拟议的修改大大提高了检测速度,准确性和计算效率,有助于提高职业安全.
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