基于卷积神经网络的语义分割算法研究

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2020-09-28 Cooperative journals: 《计算机应用研究》

熊炜 童磊 金靖熠 王传胜 王娟 曾春艳

Abstract： In order to solve the problem that the residual network can not extract image information well and the segmentation effect is poor in semantic segmentation, this paper proposed a joint feature pyramid model (JFP) to integrate the output features of the residual network, and then further extract the features in combination with the atrous spatial pyramid pooling module (ASPP) . In the decoding part, this paper applied a simple decoding structure to recover the image size to complete the semantic segmentation This paper also used attention module as the auxiliary semantic segmentation network to assist the training of the neural network. This method trains the network in the Pascal VOC 2012 data set and the enhanced Pascal VOC 2012 data set respectively, and tests it on the verification set of Pascal VOC 2012. The average ratio of intersection and Union (Miou) is 78.55% and 80.14% respectively, which shows that proposed method has good semantic segmentation performance.

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