计算机科学 ›› 2021, Vol. 48 ›› Issue (1): 175-181.doi: 10.11896/jsjkx.200200023
何彦辉1, 吴桂兴1,2, 吴志强1
HE Yan-hui1, WU Gui-xing1,2, WU Zhi-qiang1
摘要： 随着卷积神经网络的发展,X光安全检查图像的自动目标检测算法已经取得了重大进步。但是,当将这些目标检测算法应用到不同于训练集数据的新数据,即训练域数据和测试域数据的图像数据服从不一致的分布时,这些检测算法的性能通常会降低。根据X光成像的变化,提出一种基于上下文的透射率自适应域对齐方法,用于解决检测算法的域不适应问题。首先,通过利用X光图像中存在的颜色信息,设计了一种注意力机制来分别处理X光图像的每个颜色通道特征,解决不同X光机器的颜色差异问题。接着,提出一种多分辨率特征对齐方法,以解决不同厂商不同X光图像之间的数据分布差异。最后,使用上下文向量作为对抗训练的正则化,利用邻域信息提高测试精度。基于X光图像数据集和Cityscape数据集的实验表明,所提方法解决了目标检测算法在不同于训练域数据的测试域中精度下降的问题。
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