激活函数对使用YOLOv8检测造钢件缺陷的影响
Yunxia Chen1,2, Yangkai He1, Yukun Chu1
1School of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Pudong District, Shanghai 201209, China.
Materials (Basel, Switzerland)
|June 27, 2025
概括
米什激活功能显著改善了使用卷积神经网络 (CNN) 的造缺陷检测,优于其他功能. 这种增强归因于Mish Mish.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 卷积神经网络 (CNN) 对于工业应用中的自动缺陷检测至关重要.
- 在CNN中选择激活函数可以显著影响模型性能,但它们对造缺陷检测的具体影响仍未得到充分探索.
研究的目的:
- 调查不同激活函数对基于CNN的造缺陷检测性能的影响.
- 确定最佳的激活功能,以提高造缺陷识别中的精度和特征差异化.
主要方法:
- 评估了五种不同的激活函数 - - 修正线性单位 (ReLU),指数线性单位 (ELU),软增值,西格线性单位 (SiLU) 和混合.
- 实验使用YOLOv8算法进行,用于在数字放射 (DR) 图像上检测造缺陷.
- 使用特征图分析和梯度类激活映射 (Grad-CAM) 来可视化和理解模型的行为.
主要成果:
- 米什激活函数实现了最高的性能,平均平均精度 (mAP@0.5) 为90.1%.
- 软普拉斯的表现最低,实现了86.7%的mAP@0.5.
- 米什输出负值的能力增强了特征差异化和表达力,从而改善了各种造缺陷的检测.
结论:
- 与ReLU,ELU,Softplus和SiLU相比,Mish激活功能在造缺陷检测方面表现出更高的有效性.
- 米什改进了模型对DR图像中关键的灰度变化区域的关注度,从而提高了检测精度.
- 激活函数的选择是优化CNN在造缺陷分析中的性能的一个关键因素.
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