基于人工智能缺陷检测模型的木双边缺陷切割策略
Chenlong Fan1, Zilong Zhuang1, Ying Liu1
1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
这项研究介绍了木材加工的AI策略,改进了缺陷检测,并增加了12.3%的 sawn木材产量. 双边缺陷切割战略 (BDCS-YOLO) 提高了实木资源的利用率.
科学领域:
- 木材科学与技术 木材科学与技术
- 人工智能在制造业中的应用
- 木材加工自动化木材加工自动化
背景情况:
- 实木是一种有价值的建筑和家具材料.
- 木材的生长和加工会引入诸如结节和裂之类的缺陷,从而降低机械性能.
- 现有的处理方法在有效的缺陷识别和利用方面扎.
研究的目的:
- 使用人工智能开发自动木材加工战略.
- 改进实木木材缺陷的检测和定位.
- 为了提高从原木木材中切割木材的整体产量.
主要方法:
- 实施BDCS-YOLO (基于你只看一次的双边缺陷切割策略),这是用于缺陷检测的AI模型.
- 使用双面图像采集平台进行全面的木材扫描.
- 部署双面处理优化模块,用于精细的缺陷界限框和处理坐标.
主要成果:
- 在 450 张图像的数据集上,BDCS-YOLO 实现了 0.94 的平均特征检测精度.
- 双面处理优化模块提高了缺陷检测的准确性.
- 与当前的生产方法相比,木产量增加了12.3%.
结论:
- BDCS-YOLO显著提升了木材加工自动化.
- 由人工智能驱动的战略提高了对实木资源的有效利用.
- 这种方法为木材加工行业带来了巨大的改善.
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