使用Grad-CAM对二进制腐蚀图像分类进行可解释的深度学习框架
Muhammad Amir Imran Aminudin1, Mohd Na'im Abdullah1, Faizal Mustapha1
1Department of Aerospace Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
本研究引入了用于金属材料自动腐蚀检测的深度学习模型,实现高精度. 像Grad-CAM这样的可解释AI (XAI) 技术被用于可视化和验证模型预测,提高腐蚀分析的可靠性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 金属材料腐蚀带来了重要的维护和安全挑战.
- 传统的视觉检查方法效率低下,需要专家解释.
- 需要自动化解决方案来准确可靠地检测腐蚀.
研究的目的:
- 调查深度学习模型对腐蚀二进制图像分类的有效性.
- 整合可解释的人工智能 (XAI) 技术,以实现模型可解释性.
- 为了比较四个预训练的卷积神经网络 (CNN) 架构的性能.
主要方法:
- 使用了四个预先训练好的CNN:ResNet50,MobileNetV2,NASNetMobile和EfficientNetV2B0.0. 这四个CNN都是使用的.
- 在一个由9636张增强图像 (腐蚀与非腐蚀) 的数据集上训练模型.
- 应用梯度加权类激活映射 (Grad-CAM) 为XAI可视化决策.
主要成果:
- ResNet50实现了最高的分类准确性 (96.58%).
- 移动NetV2提供了最快的培训时间.
- 通过Grad-CAM,EfficientNetV2B0通过Grad-CAM在腐蚀地区显示出稳定的训练,最小的过和高激活.
结论:
- 深度学习模型,特别是CNN,显示了自动腐蚀检测的巨大潜力.
- 像Grad-CAM这样的XAI技术提高了模型透明度和对腐蚀分析的信任.
- EfficientNetV2B0和ResNet50在腐蚀分类和可解释性方面呈现出有希望的性能特征.
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