LA-U2Net: Location-Aware U2Net for Salient Object Detection

Xinliang Huang, Jiaxin Li, Yan Ding*, Pengfei Liu, Weidong Liang, Xiujuan Zhu

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Salient object detection(SOD) is particularly important especially for applications like autonomous driving which requires real-time inference speed and high performance. Most of the previous works however focus on global object accuracy but not on the connection of local objects. In this paper, we first process the cityscapes dataset into a saliency detection dataset, which focuses on distinguishing between moving objects on the road and moving objects on the sidewalk. In order to enable the saliency detection network to learn the connection between the target categories, we propose a gated convolution(GCov), which can control the input of the feature layer. For the evaluation of SOD, we combine a variety of loss functions to form a mixed loss. Equipped with the GCov and mixed loss, the proposed architecture is able to effectively distinguish the difference in the semantics of the location for the targets of the same category. Experimental results on the dataset show that our method has competitive results compared with other saliency detection networks.

源语言英语
主期刊名Eighth Symposium on Novel Photoelectronic Detection Technology and Applications
编辑Junhong Su, Lianghui Chen, Junhao Chu, Shining Zhu, Qifeng Yu
出版商SPIE
ISBN(电子版)9781510653115
DOI
出版状态已出版 - 2022
活动8th Symposium on Novel Photoelectronic Detection Technology and Applications - Kunming, 中国
期限: 7 12月 20219 12月 2021

出版系列

姓名Proceedings of SPIE - The International Society for Optical Engineering
12169
ISSN(印刷版)0277-786X
ISSN(电子版)1996-756X

会议

会议8th Symposium on Novel Photoelectronic Detection Technology and Applications
国家/地区中国
Kunming
时期7/12/219/12/21

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