基于改进SSD的无人驾驶夜间目标检测  

Unmanned night object detection based on improved SSD

作　　者：卜德飞 孙韶媛[1,2] 黄荣 王宇岚 刘致驿 BU Defei;SUN Shaoyuan;HUANG Rong;WANG Yulan;LIU Zhiyi(College of Information Science and Technology,Ministry of Education,Donghua University,Shanghai 201620,China;Engineering Research Center of Digitized Textile&Fashion Technology,Ministry of Education,Donghua University,Shanghai 201620,China)

机构地区：[1]东华大学信息科学与技术学院,上海201620 [2]东华大学数字化纺织服装技术教育部工程研究中心,上海201620

出　　处：《东华大学学报（自然科学版）》2021年第1期63-69,共7页Journal of Donghua University(Natural Science)

摘　　要：为提高SSD(single shot multibox detector)网络在夜间无人驾驶的目标检测性能,对SSD网络进行了改进:特征提取网络使用稠密连接卷积网络(Densenet)得到表征能力更强的特征图;卷积过程中进行特征图重利用,从而增加中小目标的信息;加入反卷积网络丰富特征图语义信息。试验的训练方法采用无预训练模型的训练方式,这种训练方式能够更好地拟合数据集的特性。试验结果表明,改进的SSD网络的目标检测准确率高于原SSD网络,并且优于其他主流网络。Single shot multibox detector(SSD)network was modified to improve the performance of target detection of unmanned driving at night.Feature extraction network used densely connected revolutionary networks(Densenet)to obtain more powerful feature maps.In the process of convolution,the feature map was reused to increase the information of medium and small targets.Deconvolution network was added to SSD network to enrich semantic information of feature map.The experimental training method adopted the training method without pre-training model,which could better fit the characteristics of data set.The experimental results show that the target detection accuracy of the improved SSD network is higher than that of the original SSD network,and better than other mainstream networks.

关 键 词：红外图像 目标检测 SSD网络 反卷积 

分 类 号：TN219[电子电信—物理电子学]