Adaptive Multi-modal Fusion Instance Segmentation for CAEVs in Complex Conditions: Dataset, Framework and Verifications Article Swipe
Pai Peng
,
Keke Geng
,
Guodong Yin
,
Yanbo Lu
,
Weichao Zhuang
,
Shuaipeng Liu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1186/s10033-021-00602-2
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1186/s10033-021-00602-2
Current works of environmental perception for connected autonomous electrified vehicles (CAEVs) mainly focus on the object detection task in good weather and illumination conditions, they often perform poorly in adverse scenarios and have a vague scene parsing ability. This paper aims to develop an end-to-end sharpening mixture of experts (SMoE) fusion framework to improve the robustness and accuracy of the perception systems for CAEVs in complex illumination and weather conditions. Three original contributions make our work distinctive from the existing relevant literature. The Complex KITTI dataset is introduced which consists of 7481 pairs of modified KITTI RGB images and the generated LiDAR dense depth maps, and this dataset is fine annotated in instance-level with the proposed semi-automatic annotation method. The SMoE fusion approach is devised to adaptively learn the robust kernels from complementary modalities. Comprehensive comparative experiments are implemented, and the results show that the proposed SMoE framework yield significant improvements over the other fusion techniques in adverse environmental conditions. This research proposes a SMoE fusion framework to improve the scene parsing ability of the perception systems for CAEVs in adverse conditions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s10033-021-00602-2
- https://cjme.springeropen.com/track/pdf/10.1186/s10033-021-00602-2
- OA Status
- diamond
- Cited By
- 3
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3112236305
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- OpenAlex ID
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https://openalex.org/W3112236305Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s10033-021-00602-2Digital Object Identifier
- Title
-
Adaptive Multi-modal Fusion Instance Segmentation for CAEVs in Complex Conditions: Dataset, Framework and VerificationsWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-30Full publication date if available
- Authors
-
Pai Peng, Keke Geng, Guodong Yin, Yanbo Lu, Weichao Zhuang, Shuaipeng LiuList of authors in order
- Landing page
-
https://doi.org/10.1186/s10033-021-00602-2Publisher landing page
- PDF URL
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https://cjme.springeropen.com/track/pdf/10.1186/s10033-021-00602-2Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://cjme.springeropen.com/track/pdf/10.1186/s10033-021-00602-2Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Robustness (evolution), Parsing, Segmentation, Perception, Fusion, Computer vision, Annotation, Machine learning, Biochemistry, Gene, Philosophy, Linguistics, Neuroscience, Biology, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 1, 2024: 1, 2023: 1Per-year citation counts (last 5 years)
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65Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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