基于加权损失函数的多尺度对抗网络图像语义分割算法

A MULTI-SCALE ADVERSARIAL NETWORK SEMANTIC SEGMENTATION ALGORITHM BASED ON WEIGHTED LOSS FUNCTION

针对现有的语义分割算法存在分割结果空间不一致的问题,提出一种基于加权损失函数的多尺度对抗网络语义分割算法。在DeepLab v3基本框架的基础上,引入Pix2pix网络作为生成对抗网络模型,实现多尺度对抗网络语义分割。同时,为增加模型的泛化能力与训练精度,提出将传统的多分类交叉熵损失函数与生成器输出的内容损失函数和鉴别器输出的对抗损失函数相结合,构建加权损失函数。大量定性定量实验结果表明,该算法能够识别并分割细小的物体,其语义分割性能超过现有的深度网络,在保证语义分割空间一致性的同时提高了分割效率。

Aiming at the problem that the result space of existing semantic segmentation algorithm is inconsistent,this paper proposes a multi-scale adversarial network semantic segmentation algorithm based on multi-scale weighted loss function.It introduced Pix2pix network as the generation adversarial model on the basic framework of DeepLab V3.In addition,in order to improve the generalization ability and training accuracy of the model,we combined the traditional multi-classification cross-entropy loss function with the content loss function of generator and the adversarial loss function of discriminator to construct a weighted loss function.A large number of qualitative and quantitative experimental results show that the performance of our algorithm is better than the existing algorithms,and it can improve the detection efficiency while ensuring the semantic segmentation spatial consistency.