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- Title
Progressive Domain Adaptive Object Detection Based on Self‐Attention in Foggy Weather.
- Authors
Lin, Meng; Zhou, Gang; Yang, Yawei; Shi, Jun
- Abstract
The quality of captured images is compromised by foggy weather, resulting in poor performance of detection networks. To mitigate this issue, researchers have attempted to employ domain adaptation techniques. However, a significant difference between the domains can create difficulties in the adaptation process, resulting in unstable training procedures and less‐than‐optimal outcomes. A self‐attention‐based progressive domain adaptive object detection framework is proposed to address this issue. We solved this problem by building an intermediate domain through physical synthesis. Moreover, the domain features of different levels are aligned in the feature space to achieve a better domain adaptation effect. In addition, we add a plug‐and‐play attention module to enhance the feature discriminability in the shallow feature extraction stage. Our method performs better than the existing state‐of‐the‐art techniques in synthetic and real‐world datasets, according to the experimental results. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
- Subjects
JAPAN; OBJECT recognition (Computer vision); FEATURE extraction; WEATHER; RESEARCH personnel; NETWORK performance
- Publication
IEEJ Transactions on Electrical & Electronic Engineering, 2023, Vol 18, Issue 12, p1923
- ISSN
1931-4973
- Publication type
Article
- DOI
10.1002/tee.23922