山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (6): 19-28.doi: 10.6040/j.issn.1672-3961.0.2023.220
• 机器学习与数据挖掘 • 上一篇
张曼,孙凯军*,李翔,孙纪舟
ZHANG Man, SUN Kaijun*, LI Xiang, SUN Jizhou
摘要： 针对工业场景下检测模型参数量多、安全绳易与背景混淆问题,提出一种融合FasterNet和RepVGG的目标检测算法(you only look once version 5 fasternet-repvgg-cutmix-bifpn, YOLOv5s-FRCB)。通过引入轻量级网络结构FasterNet Block和RepVGG Block替换YOLOv5s部分卷积层,显著减少模型参数量和加快检测速度,满足实时性需求;通过BiFPN特征强连通性提升模型特征学习能力;改进Cutmix数据增强方法,随机将目标嵌入输入图像,更新标签,缓解标签类别不平衡问题,提高泛化性。在自建安全设备佩戴检测数据集上进行试验,结果表明:YOLOv5s-FRCB mAP值达到了96.3%,算法模型内存减少34%,是一种高效实用的安全设备佩戴检测方法;YOLOv5s-FRCB能在保证准确率的同时,进一步降低计算量。
中图分类号:
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