碳纤维单向带Prepreg的缺陷检测方法基于增强的YOLOv8s
Weipeng Su1,2, Mei Sang1,2, Yutong Liu1,2
1School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究通过改进的YOLOv8s模型和注意力机制来提高碳纤维预制材料的缺陷检测. 新型号准确地识别了小型和长长的缺陷,改善了表面质量控制.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 现有的碳纤维预制材料表面缺陷检测面临着小目标和延长缺陷的挑战.
- 当前方法中的不准确性阻碍了碳纤维材料的精确质量控制.
研究的目的:
- 开发基于YOLOv8s的增强型号,以改进碳纤维预制材料表面缺陷检测.
- 为了解决检测小型和大型尺寸比例缺陷的局限性.
主要方法:
- 将全球注意力机制 (GAM) 和可变形大型内核注意力 (DLKA) 集成到 YOLOv8s 架构中.
- 在脊柱和检测头之间插入注意力机制,以增强特征提取.
- 评估用于缺陷识别的YOLOv8s-GAM和YOLOv8s-DLKA模型.
主要成果:
- YOLOv8s-GAM实现了84.4%的mAP@0.5,79.3%的精度和78.3%的回忆.
- YOLOv8s-DLKA实现了86.4%的mAP@0.5,82.4%的精度和80.2%的回忆.
- 这两种模型都比原始YOLOv8s显著改进,特别是在缺陷检测的精度和回忆方面.
结论:
- 增强的YOLOv8s模型与GAM和DLKA提供有效的解决方案,用于精确识别碳纤维预制表面上的缺陷.
- 拟议的模型提高了对具有挑战性的缺陷类型的检测精度,提高了质量保证.
- 这些进步有助于碳纤维复合材料的更可靠的制造工艺.
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