An Object Detection Method Based on Feature Uncertainty Domain Adaptation for Autonomous Driving
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Published:2023-05-25
Issue:11
Volume:13
Page:6448
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Zhu Yuan1, Xu Ruidong1ORCID, Tao Chongben2, An Hao1, Sun Zhipeng3, Lu Ke1ORCID
Affiliation:
1. School of Automotive Studies, Tongji University, Shanghai 201800, China 2. The School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China 3. Nanchang Automotive Institute of Intelligence & New Energy, Tongji University, Nanchang 330013, China
Abstract
The environment perception algorithm in autonomous driving is trained in the source domain, leading to domain drift and reduced detection accuracy in the target domain due to shifts in background feature distribution. To address this issue, a domain adaptive object detection algorithm based on feature uncertainty is proposed, which can improve the detection performance of object detection algorithms in unlabeled data. Firstly, a local alignment module based on channel information is proposed, which can obtain the model’s uncertainty about different domain data based on the feature channels obtained through the feature extraction network, achieving adaptive dynamic local alignment. Secondly, an instance-level alignment module guided by local feature uncertainty is proposed, which can obtain the corresponding instance-level uncertainty through ROI mapping. To improve the domain invariance of bounding box regression, a multi-class, multi-regression instance-level uncertainty alignment module is proposed, which can achieve spatial decoupling of classification and regression tasks, further improving the model’s domain adaptive ability. Finally, the effectiveness of the proposed algorithm is validated on Cityscapes, KITTI, and real vehicle data.
Funder
Perspective Study Funding of Nanchang Automotive Institute of Intelligence and New Energy, Tongji University
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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