一个改进的轻量级YOLOv11算法用于接表面缺陷检测
Runmei Zhang1, Chenfei Pan1, Zihua Chen2
1School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, Anhui, 230601, China.
Scientific reports
|March 1, 2026
概括
本研究介绍了YOLO-Air,这是一个改进的轻量级YOLOv11模型,用于检测接表面缺陷. 该模型提高了准确性,同时显著降低了计算负载和参数,提供了具有成本效益的解决方案.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 工业接面临着不清楚缺陷特征和复杂背景的挑战.
- 现有的缺陷检测模型通常是昂贵的,在接表面缺陷检测方面缺乏效率.
研究的目的:
- 提出一个改进的,轻量级的YOLOv11模型 (YOLO-Air),用于增强接表面缺陷检测.
- 解决现有模型在成本,复杂性和检测准确性方面的局限性.
主要方法:
- 整合特征提取和卷积模块,以提高特征表示和效率.
- 将GSConv和VOV-GSCSP模块集成到子网络中,以减少功能冗余和计算负载.
- 轻量级检测头的设计,以进一步降低模型的复杂性.
主要成果:
- 在接缺陷测试-V2和NEU-DET数据集上,YOLO-Air模型表现出卓越的性能.
- 在mAP50指标上取得了1.3%的改善.
- 将参数数量减少了17.3%,计算复杂度减少了31.7%.
结论:
- 拟议的YOLO-Air模型为接表面缺陷检测提供了有效和高效的解决方案.
- 该型号的轻量级设计和改进的性能使其成为工业应用的可行选择.
- 实验数据证实了YOLO-Air模型性能的稳定性.
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