吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (3): 633-639.doi: 10.13229/j.cnki.jdxbgxb20200871
• 计算机科学与技术 • 上一篇
Xiang-jun LI1,2(),Jie-ying TU1,Zhi-bin ZHAO1()
摘要:
针对熔解曲线图像中峰值分类的问题,提出了一种基于多尺度融合的卷积神经网络(CNN)分类模型。首先,将通过空洞卷积获取到的多尺度上下文信息与残差模块提取到的特征信息相融合,弥补了深层网络丢失全局信息的缺点。不同于传统卷积采用单一卷积核的方式,本文使用了一种卷积核的权值随输入而变化的动态滤波器,从而提高了网络学习的准确性。此外,为了提高模型的泛化能力,本文创建了熔解曲线的数据集,其中包括平衡数据集和不平衡数据集。将6个基于深度学习的分类方法与本文方法进行比较,结果表明本文方法在客观评价指标上明显优于其他方法。
中图分类号:
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