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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. <\/jats:p>","DOI":"10.1142\/s0218126623502638","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T06:50:32Z","timestamp":1679035832000},"source":"Crossref","is-referenced-by-count":2,"title":["Semantic Segmentation Algorithm of Night Images Based on Attention Mechanism"],"prefix":"10.1142","volume":"32","author":[{"given":"Xiaona","family":"Xie","sequence":"first","affiliation":[{"name":"College of Automation, Chengdu University of Information Technology, Chengdu, Sichuan, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyong","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Automation, Chengdu University of Information Technology, Chengdu, Sichuan, P. R. 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