计算机工程与应用2024，Vol.60Issue(19)：221-229,9.DOI:10.3778/j.issn.1002-8331.2307-0036
DL-GAN生成对抗网络的半监督语义分割模型
DL-GAN Semi Supervised Semantic Segmentation Model for Generative Adversarial Network
摘要
Abstract
Semantic segmentation is currently the mainstream fully supervised learning method,and the quality and quan-tity of data determine the training effect of the network.High-quality and large-scale training data can be obtained only by spending a lot of annotation costs.Based on the above situation,semantic segmentation based on semi-supervised learn-ing emerged.Semi-supervised learning can save the cost of labeling data and solve the problem of requiring a large amount of labeling costs.More and more people are beginning to pay attention to semi-supervised learning for image semantic segmentation.Based on the current development status of image semantic segmentation methods,a semi-supervised semantic segmentation model combining DeepLabv2's generative adversarial network(DL-GAN)is proposed.First,it uses DeepLabv2 as the generator network of the generative adversarial network,and a fully convolutional network as the dis-criminator network of the generator network.Secondly,it improves the generation network by applying the CBAM atten-tion mechanism and deep separable convolution for the first time to DeepLabv2 as a generation network.Specifically,it adds the CBAM attention mechanism before the final convolutional layer of DeepLabv2,and replaces the standard convo-lution of Resnet residual blocks in the DeepLabv2 network with deep separable convolution,which makes the weight pa-rameters of the entire model more reasonably distributed,improves the model's representation ability and computational efficiency,and accelerates the training efficiency.Finally,replacing the standard convolution of the discriminator with a hole convolution improves the receptive field of the entire discriminator,enhances training effectiveness,and improves se-mantic segmentation accuracy.The experimental results of the proposed method on the PASCAL VOC 2012 dataset show an average improvement of 6.3 percentage points compared to the Affinitynet network,proving the effectiveness of the proposed method.关键词
生成对抗网络/注意力机制/语义分割/深度可分离卷积Key words
generative adversarial network/attention mechanism/semantic segmentation/deep separable convolution分类
信息技术与安全科学引用本文复制引用
刘凡,段先华,胡维康..DL-GAN生成对抗网络的半监督语义分割模型[J].计算机工程与应用,2024,60(19):221-229,9.基金项目
国家自然科学基金(61806087) （61806087）
江苏省研究生科研与实践创新计划项目(KYCX21_3489). （KYCX21_3489）
