用增强的YOLOv8进行合金多缺陷检测和分类
Ying Han1, Xingkun Li1, Gongxiang Cui1
1Naval Architecture and Port Engineering College, Shandong Jiaotong University Weihai, Weihai, Shandong, People's Republic of China.
PloS one
|March 20, 2025
概括
本研究介绍了一种增强的YOLOv8模型,用于检测合金缺陷,提高准确性和降低模型复杂性. 新方法显著提高了各种缺陷的检测率,提高了行业的产品安全.
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:
- 合金缺陷会影响结构完整性和安全性.
- 目前的缺陷检测方法缺乏准确性和精确的定位.
研究的目的:
- 为改进合金缺陷检测和分类提出一个增强的YOLOv8-ALGP模型.
- 解决现有方法中精度和目标框架的局限性.
主要方法:
- 数据集增强了3个额外的缺陷,以改善通用化.
- 集成ALGC3模块,结合Ghost卷积和残余连接,用于模型轻量化.
- 重建脊柱网络,增强部层,以改善特征提取.
主要成果:
- 模型参数从30万以上减少到16万.
- 与YOLOv8.3相比,平均检测准确度从64.5%提高到71.3%.
- 显著提高了特定缺陷的检测准确性,例如消失 (21.6%至32.2%) 和喷射 (38.5%至60%).
结论:
- 拟议的YOLOv8-ALGP方法有效地检测和分类合金表面缺陷.
- 增强型号提供了更好的准确性和效率,有助于提高行业的产品性能.
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