PPLA-变压器:一种高效的变压器,用于基于金字塔聚合的线性注意力检测缺陷
Xiaona Song1, Yubo Tian1, Haichao Liu1
1School of Mechanical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
本研究介绍了一种有效的深度学习模型,用于工业缺陷检测,提高准确性和速度. 新方法改进了Swin-Transformer,提供更好的性能,降低了质量控制的计算成本.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 工业质量控制 工业质量控制
背景情况:
- 工业产品的微妙缺陷挑战了传统的质量控制,要求高精度和效率.
- 像Swin-Transformer这样的深度学习模型是有前途的,但它们面临的计算负担限制了它们的工业应用.
- 现有的方法难以平衡检测精度与工业环境的高速要求.
研究的目的:
- 为工业表面缺陷检测开发一种新的深度学习模型,以提高准确性和效率.
- 为了解决Swin-Transformer在工业缺陷检测任务中的计算局限性.
- 改进全球和本地特征的提取,以便更精确地识别缺陷.
主要方法:
- 提出了一种新型模型,采用线性注意力机制与金字塔聚合来减少计算负载并提高效率.
- 集成的部分卷积可增强局部特征提取并进一步提高检测精度.
- 在自建的SIM卡插槽缺陷数据集和公开的PKU-Market-PCB数据集上对模型进行了评估.
主要成果:
- 与Swin-Transformer相比,拟议的模型在两个数据集上都取得了更高的性能.
- 在最小的计算成本下,在平均平均精度 (mAP) 和每秒 (FPS) 中显著改进.
- 在SIM卡插槽数据集上表现比Swin-Transformer高1.2%mAP和52FPS,在PKU-Market-PCB数据集上表现比Swin-Transformer高1.7%mAP和51FPS.
结论:
- 开发的带有金字塔聚合的线性注意力机制有效地提高了工业缺陷检测的准确性和效率.
- 该模型在降低计算成本的同时提高性能的能力验证了其在工业质量控制中的实际应用.
- 拟议的方法显示了普遍性和相对于现有的Swin-变压器模型在表面缺陷检测方面的显著优势.
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