SGN-YOLO:通过基于半全球网络的改进YOLOv5检测木材缺陷
Wei Meng1,2, Yilin Yuan1,2
1College of Information, Beijing Forestry University, Beijing 100083, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
这项研究引入了一种改进的YOLOv5模型用于木材缺陷检测. 新的半全球网络提高了识别关键木材缺陷的准确性和效率.
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:
- 准确的木材缺陷检测对于木制品的转化至关重要.
- 现有的物体检测模型因效率低下的特征提取和有限的上下文信息而难以处理小而多样化的木材缺陷.
研究的目的:
- 为改善木材缺陷检测提出一个增强的YOLOv5模型.
- 解决当前模型在准确识别关键木材缺陷方面的局限性.
主要方法:
- 将轻量级的半全球网络 (SGN) 集成到 YOLOv5 骨干中,用于全球上下文建模.
- 嵌入扩展高效层聚合网络 (E-ELAN),以提高网络学习能力.
- 利用高效交叉和合并 (EIOU) 损失来加快趋同并完善回归精度.
主要成果:
- 拟议的YOLOv5-SGN模型在公共木材缺陷数据集上实现了86.4%的平均平均精度 (mAP).
- 与基线模型相比显示了显著的改进:比基线网络有3.1%,比SSD有7.1%,比Faster R-CNN有13.6%.
- 验证了模型在准确检测木材缺陷方面的有效性.
结论:
- 建议使用SGN的YOLOv5模型有效地提高了木材缺陷检测的准确性和效率.
- 该方法为识别关键木材缺陷提供了强大的解决方案,其性能优于现有的最先进模型.
- 这种方法有助于推进木制品制造业的自动化质量控制.
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