一个实时缺陷检测器,具有内容引导的功能提取和细节增强
Yuefei Huang1, Tingting Fang1, Ming Ye1
1College of Artificial Intelligence, Southwest University, Chongqing, China.
Annals of the New York Academy of Sciences
|November 3, 2025
概括
本研究介绍了CGRNet,这是一个高效的实时探测器,用于工业表面缺陷检测. CGRNet提高了小缺陷的识别,并实现了高准确性,解决复杂的背景和错过的缺陷.
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:
- 工业表面缺陷检测受到复杂的背景和小,容易被忽视的缺陷的挑战.
- 现有的方法经常在准确性和实时处理方面扎.
研究的目的:
- 开发一个高效和准确的实时检测器,用于工业表面缺陷.
- 改进检测小缺陷,处理复杂的背景.
主要方法:
- 推出了CGRNet,这是一个以内容为导向的特征提取网络,具有自适应的高频过.
- 采用了一个细节增强模块,具有双重剩余注意力机制,用于多尺度特征交互.
- 利用SIoU损失函数和Lion优化器来实现更快的融合和更好的本地化.
主要成果:
- 在PVEL_AD数据集上,CGRNet实现了93.6%的mAP,超过了现有方法.
- 该模型在NEU-DET数据集上以每秒81.9的实时性能.
- 在检测小缺陷和处理复杂背景方面观察到显著的改进.
结论:
- CGRNet为实时工业表面缺陷检测提供了有效的解决方案.
- 提出的方法提高了准确性和处理速度,满足了实际应用的需求.
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