Semantic Segmentation Algorithm of Night Images Based on Attention Mechanism

被引:1
|
作者
Xie, Xiaona [1 ]
Xu, Zhiyong [1 ]
Jiang, Tao [1 ]
Yuan, JianYing [1 ]
Chang, Zhengwei [2 ]
Zhang, Linghao [2 ]
机构
[1] Chengdu Univ Informat Technol, Chengdu, Sichuan, Peoples R China
[2] State Grid Sichuan Elect Power Res Inst, Chengdu, Sichuan, Peoples R China
关键词
Semantic segmentation; thermal infrared images; visible light images; channel spatial attention mechanism; feature fusion; NETWORK;
D O I
10.1142/S0218126623502638
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
At present, there are many semantic segmentation algorithms with excellent performance for intelligent driving vehicles, but most of them only work well on scenes with good illumination. In order to solve the problem of scene segmentation under low illumination, this paper proposes a novel semantic segmentation algorithm that combines visible and infrared images. In this algorithm, two parallel encoders are designed as the input of the images, and the decoder divides the fused images output from the encoder. The model is based on ResNet algorithm, and the residual attention module is used in each branch to mine and enhance the spatial features of multilevel channels to extract images information. Experiments are carried out on publicly available thermal infrared and visible datasets. The results show that the algorithm proposed in this paper is superior to the algorithm using only visible images in semantic segmentation of traffic environment.
引用
收藏
页数:20
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