MFF-Net: Multimodal Feature Fusion Network for 3D Object Detection Article Swipe
Peicheng Shi
,
Zhi-Qiang Liu
,
Heng Qi
,
Aixi Yang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2023.037794
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2023.037794
In complex traffic environment scenarios, it is very important for autonomous vehicles to accurately perceive the dynamic information of other vehicles around the vehicle in advance. The accuracy of 3D object detection will be affected by problems such as illumination changes, object occlusion, and object detection distance. To this purpose, we face these challenges by proposing a multimodal feature fusion network for 3D object detection (MFF-Net). In this research, this paper first uses the spatial transformation projection algorithm to map the image features into the feature space, so that the image features are in the same spatial dimension when fused with the point cloud features. Then, feature channel weighting is performed using an adaptive expression augmentation fusion network to enhance important network features, suppress useless features, and increase the directionality of the network to features. Finally, this paper increases the probability of false detection and missed detection in the non-maximum suppression algorithm by increasing the one-dimensional threshold. So far, this paper has constructed a complete 3D target detection network based on multimodal feature fusion. The experimental results show that the proposed achieves an average accuracy of 82.60% on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset, outperforming previous state-of-the-art multimodal fusion networks. In Easy, Moderate, and hard evaluation indicators, the accuracy rate of this paper reaches 90.96%, 81.46%, and 75.39%. This shows that the MFF-Net network has good performance in 3D object detection.
Related Topics
Concepts
Computer science
Artificial intelligence
Feature (linguistics)
Weighting
Point cloud
Computer vision
Pattern recognition (psychology)
Object detection
Object (grammar)
Fusion
Backbone network
Image fusion
Dimension (graph theory)
Image (mathematics)
Mathematics
Pure mathematics
Linguistics
Medicine
Radiology
Philosophy
Computer network
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2023.037794
- OA Status
- diamond
- Cited By
- 3
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4371766304
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4371766304Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.32604/cmc.2023.037794Digital Object Identifier
- Title
-
MFF-Net: Multimodal Feature Fusion Network for 3D Object DetectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Peicheng Shi, Zhi-Qiang Liu, Heng Qi, Aixi YangList of authors in order
- Landing page
-
https://doi.org/10.32604/cmc.2023.037794Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.32604/cmc.2023.037794Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Feature (linguistics), Weighting, Point cloud, Computer vision, Pattern recognition (psychology), Object detection, Object (grammar), Fusion, Backbone network, Image fusion, Dimension (graph theory), Image (mathematics), Mathematics, Pure mathematics, Linguistics, Medicine, Radiology, Philosophy, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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58Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.map | 79 |
| abstract_inverted_index.the | 15, 22, 73, 80, 84, 89, 94, 101, 128, 131, 139, 148, 154, 179, 188, 212, 226 |
| abstract_inverted_index.This | 223 |
| abstract_inverted_index.face | 51 |
| abstract_inverted_index.far, | 158 |
| abstract_inverted_index.good | 230 |
| abstract_inverted_index.hard | 209 |
| abstract_inverted_index.into | 83 |
| abstract_inverted_index.rate | 214 |
| abstract_inverted_index.same | 95 |
| abstract_inverted_index.show | 177 |
| abstract_inverted_index.such | 37 |
| abstract_inverted_index.that | 88, 178, 225 |
| abstract_inverted_index.this | 48, 67, 69, 136, 159, 216 |
| abstract_inverted_index.uses | 72 |
| abstract_inverted_index.very | 7 |
| abstract_inverted_index.when | 98 |
| abstract_inverted_index.will | 32 |
| abstract_inverted_index.with | 100 |
| abstract_inverted_index.Easy, | 206 |
| abstract_inverted_index.Then, | 105 |
| abstract_inverted_index.based | 169 |
| abstract_inverted_index.cloud | 103 |
| abstract_inverted_index.false | 142 |
| abstract_inverted_index.first | 71 |
| abstract_inverted_index.fused | 99 |
| abstract_inverted_index.image | 81, 90 |
| abstract_inverted_index.other | 19 |
| abstract_inverted_index.paper | 70, 137, 160, 217 |
| abstract_inverted_index.point | 102 |
| abstract_inverted_index.shows | 224 |
| abstract_inverted_index.these | 52 |
| abstract_inverted_index.using | 111 |
| abstract_inverted_index.82.60% | 186 |
| abstract_inverted_index.Toyota | 194 |
| abstract_inverted_index.around | 21 |
| abstract_inverted_index.fusion | 59, 116, 203 |
| abstract_inverted_index.missed | 145 |
| abstract_inverted_index.object | 30, 41, 44, 63, 234 |
| abstract_inverted_index.space, | 86 |
| abstract_inverted_index.target | 166 |
| abstract_inverted_index.(KITTI) | 197 |
| abstract_inverted_index.75.39%. | 222 |
| abstract_inverted_index.81.46%, | 220 |
| abstract_inverted_index.90.96%, | 219 |
| abstract_inverted_index.MFF-Net | 227 |
| abstract_inverted_index.average | 183 |
| abstract_inverted_index.channel | 107 |
| abstract_inverted_index.complex | 1 |
| abstract_inverted_index.dynamic | 16 |
| abstract_inverted_index.enhance | 119 |
| abstract_inverted_index.feature | 58, 85, 106, 172 |
| abstract_inverted_index.fusion. | 173 |
| abstract_inverted_index.network | 60, 117, 121, 132, 168, 228 |
| abstract_inverted_index.reaches | 218 |
| abstract_inverted_index.results | 176 |
| abstract_inverted_index.spatial | 74, 96 |
| abstract_inverted_index.traffic | 2 |
| abstract_inverted_index.useless | 124 |
| abstract_inverted_index.vehicle | 23 |
| abstract_inverted_index.Finally, | 135 |
| abstract_inverted_index.accuracy | 27, 184, 213 |
| abstract_inverted_index.achieves | 181 |
| abstract_inverted_index.adaptive | 113 |
| abstract_inverted_index.advance. | 25 |
| abstract_inverted_index.affected | 34 |
| abstract_inverted_index.changes, | 40 |
| abstract_inverted_index.complete | 164 |
| abstract_inverted_index.dataset, | 198 |
| abstract_inverted_index.features | 82, 91 |
| abstract_inverted_index.increase | 127 |
| abstract_inverted_index.perceive | 14 |
| abstract_inverted_index.previous | 200 |
| abstract_inverted_index.problems | 36 |
| abstract_inverted_index.proposed | 180 |
| abstract_inverted_index.purpose, | 49 |
| abstract_inverted_index.suppress | 123 |
| abstract_inverted_index.vehicles | 11, 20 |
| abstract_inverted_index.Institute | 190, 196 |
| abstract_inverted_index.Karlsruhe | 189 |
| abstract_inverted_index.Moderate, | 207 |
| abstract_inverted_index.algorithm | 77, 151 |
| abstract_inverted_index.detection | 31, 45, 64, 143, 146, 167 |
| abstract_inverted_index.dimension | 97 |
| abstract_inverted_index.distance. | 46 |
| abstract_inverted_index.features, | 122, 125 |
| abstract_inverted_index.features. | 104, 134 |
| abstract_inverted_index.important | 8, 120 |
| abstract_inverted_index.increases | 138 |
| abstract_inverted_index.networks. | 204 |
| abstract_inverted_index.performed | 110 |
| abstract_inverted_index.proposing | 55 |
| abstract_inverted_index.research, | 68 |
| abstract_inverted_index.weighting | 108 |
| abstract_inverted_index.(MFF-Net). | 65 |
| abstract_inverted_index.Technology | 192 |
| abstract_inverted_index.accurately | 13 |
| abstract_inverted_index.autonomous | 10 |
| abstract_inverted_index.challenges | 53 |
| abstract_inverted_index.detection. | 235 |
| abstract_inverted_index.evaluation | 210 |
| abstract_inverted_index.expression | 114 |
| abstract_inverted_index.increasing | 153 |
| abstract_inverted_index.multimodal | 57, 171, 202 |
| abstract_inverted_index.occlusion, | 42 |
| abstract_inverted_index.projection | 76 |
| abstract_inverted_index.scenarios, | 4 |
| abstract_inverted_index.threshold. | 156 |
| abstract_inverted_index.constructed | 162 |
| abstract_inverted_index.environment | 3 |
| abstract_inverted_index.indicators, | 211 |
| abstract_inverted_index.information | 17 |
| abstract_inverted_index.non-maximum | 149 |
| abstract_inverted_index.performance | 231 |
| abstract_inverted_index.probability | 140 |
| abstract_inverted_index.suppression | 150 |
| abstract_inverted_index.augmentation | 115 |
| abstract_inverted_index.experimental | 175 |
| abstract_inverted_index.illumination | 39 |
| abstract_inverted_index.Technological | 195 |
| abstract_inverted_index.outperforming | 199 |
| abstract_inverted_index.directionality | 129 |
| abstract_inverted_index.transformation | 75 |
| abstract_inverted_index.one-dimensional | 155 |
| abstract_inverted_index.state-of-the-art | 201 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.61705302 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |