计算机科学 ›› 2020, Vol. 47 ›› Issue (5): 161-165.doi: 10.11896/jsjkx.190300062
乔梦雨, 王鹏, 吴娇, 张宽
QIAO Meng-yu, WANG Peng, WU Jiao, ZHANG Kuan
摘要： 在实际陆战场环境中,作战人员无法随身携带GPU等大型计算设备,因此较难计算规模较大的神经网络参数,进而导致目标识别网络无法实时工作。现有的轻量级神经网络虽然解决了实时性的问题,但是不能满足准确率的要求。为此,文中提出了一种基于轻量级卷积神经网络的目标识别算法(E-MobilNet)。为了提升网络学习的效果,以现有深度学习的主要目标检测框架MobileNet-V2为基础,插入一种ELU函数作为激活函数。首先,使用扩张卷积来增加通道数,以获得更多的特征;接着,通过ELU函数激活输出特征,这样可以缓解线性部分的梯度消失,并且使非线性部分对输入变化的噪声更鲁棒;然后,通过残差连接的方式组合高层特征与低层特征的输出;最后,将全局池化的输出结果输入Softmax分类函数。实验数据表明,在同样的测试集和测试环境下,与现在主流的轻量级深度学习目标识别算法相比,E-MobileNet识别的准确率和每秒检测的帧率都有所提升。实验数据充分说明,使用ELU激活函数和全局池化层减少了参数的数量,增强了模型的泛化能力,提升了算法的鲁棒性,在保证神经网络模型轻量级的基础上有效地提高了目标的识别准确率。
中图分类号:
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