| 基于改进BiSeNet的室内场景语义分割方法 |
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| 引用本文： | 张立国,程瑶,金梅,王娜.基于改进BiSeNet的室内场景语义分割方法[J].计量学报,2021,42(4):515-520. |
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| 作者姓名： | 张立国 程瑶 金梅 王娜 |
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| 作者单位： | 燕山大学电气工程学院,河北秦皇岛066004 |
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| 基金项目： | 河北省引进国外智力项目(基于多源视觉融合的病房智能看护系统);河北省重点研究计划;国家重点研发计划;河北省军民融合产业发展专项资金 |
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| 摘 要： | 室内场景的语义分割一直是深度学习语义分割领域的一个重要方向.室内语义分割主要存在的问题有语义类别多、很多物体类会有相互遮挡、某些类之间相似性较高等.针对这些问题,提出了一种用于室内场景语义分割的方法.该方法在BiSeNet(bilateral segmentation network)的网络结构基础上,引入了一个空洞金...
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| 关 键 词： | 计量学 语义分割 特征融合 室内场景 BiSeNet |
| 收稿时间： | 2020-03-16 |
A Method of Semantic Segmentation for Indoor Scenes Based on Improved BiSeNet |
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| ZHANG Li-guo,CHENG Yao,JIN Mei,WANG Na.A Method of Semantic Segmentation for Indoor Scenes Based on Improved BiSeNet[J].Acta Metrologica Sinica,2021,42(4):515-520. |
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| Authors: | ZHANG Li-guo CHENG Yao JIN Mei WANG Na |
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| Affiliation: | Institute of Electrical Engineering, YanShan University, Qinhuangdao, Hebei 066004, China |
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| Abstract: | Semantic segmentation of indoor scenes has always been an important direction in the field of deep learning semantic segmentation. The main problems of indoor scene segmentation are many semantic categories, many object classes will block each other, and some classes have high similarity. Aiming at these problems, Proposed a method for semantic segmentation of indoor scenes which is based on the BiSeNet (bilateral segmentation network), this method introduces a hollow pyramid pooling layer and a multi-scale feature fusion module. The features are fused to obtain enhanced content features, which improves the models performance for semantic segmentation of indoor scenes. The MIoU performance of this method on the indoor scene dataset in ADE20K increased by 23.5% compared toSegNet and 3.5% compared to before model. |
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| Keywords: | metrology semantic segmentation feature fusion indoor scene BiSeNet |
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