Mosaic-Mixed Attention-Based Unexpected Traffic Scene Classification
With the rapid advancements in artificial intelligence and smart mobility technologies, traffic monitoring systems are evolving quickly. Among these systems, in-vehicle monitoring systems using Object Detection (OD) algorithms are gaining attention for identifying traffic participants in distress. H...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10844855/ |
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| _version_ | 1832583958530883584 |
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| author | Sang-Hyun Lee Soomok Lee Ilsoo Yun |
| author_facet | Sang-Hyun Lee Soomok Lee Ilsoo Yun |
| author_sort | Sang-Hyun Lee |
| collection | DOAJ |
| description | With the rapid advancements in artificial intelligence and smart mobility technologies, traffic monitoring systems are evolving quickly. Among these systems, in-vehicle monitoring systems using Object Detection (OD) algorithms are gaining attention for identifying traffic participants in distress. However, current OD algorithms often underperform in complex or unexpected traffic scenarios, such as accidents. In this study, we propose a novel scene classification network that integrates OD with a cross-attention mechanism By leveraging spatial mosaic and mixed attention mechanisms, the network emphasizes spatial relationships and inter-channel correlations, significantly enhancing accuracy in identifying critical traffic events. The detailed evaluation demonstrates improved efficiency and accuracy, underscoring the potential of the system for future traffic incident classification. |
| format | Article |
| id | doaj-art-0a421eba90ce4a55853b1b75697758f9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0a421eba90ce4a55853b1b75697758f92025-01-28T00:01:15ZengIEEEIEEE Access2169-35362025-01-0113157121572210.1109/ACCESS.2025.353112110844855Mosaic-Mixed Attention-Based Unexpected Traffic Scene ClassificationSang-Hyun Lee0Soomok Lee1https://orcid.org/0000-0001-6633-997XIlsoo Yun2https://orcid.org/0000-0001-5618-7933Department of Data, Network, and AI, Ajou University, Suwon-si, South KoreaDepartment of Data, Network, and AI, Ajou University, Suwon-si, South KoreaDepartment of Data, Network, and AI, Ajou University, Suwon-si, South KoreaWith the rapid advancements in artificial intelligence and smart mobility technologies, traffic monitoring systems are evolving quickly. Among these systems, in-vehicle monitoring systems using Object Detection (OD) algorithms are gaining attention for identifying traffic participants in distress. However, current OD algorithms often underperform in complex or unexpected traffic scenarios, such as accidents. In this study, we propose a novel scene classification network that integrates OD with a cross-attention mechanism By leveraging spatial mosaic and mixed attention mechanisms, the network emphasizes spatial relationships and inter-channel correlations, significantly enhancing accuracy in identifying critical traffic events. The detailed evaluation demonstrates improved efficiency and accuracy, underscoring the potential of the system for future traffic incident classification.https://ieeexplore.ieee.org/document/10844855/Traffic monitoringscene classificationattention networkimage mosaic processing |
| spellingShingle | Sang-Hyun Lee Soomok Lee Ilsoo Yun Mosaic-Mixed Attention-Based Unexpected Traffic Scene Classification IEEE Access Traffic monitoring scene classification attention network image mosaic processing |
| title | Mosaic-Mixed Attention-Based Unexpected Traffic Scene Classification |
| title_full | Mosaic-Mixed Attention-Based Unexpected Traffic Scene Classification |
| title_fullStr | Mosaic-Mixed Attention-Based Unexpected Traffic Scene Classification |
| title_full_unstemmed | Mosaic-Mixed Attention-Based Unexpected Traffic Scene Classification |
| title_short | Mosaic-Mixed Attention-Based Unexpected Traffic Scene Classification |
| title_sort | mosaic mixed attention based unexpected traffic scene classification |
| topic | Traffic monitoring scene classification attention network image mosaic processing |
| url | https://ieeexplore.ieee.org/document/10844855/ |
| work_keys_str_mv | AT sanghyunlee mosaicmixedattentionbasedunexpectedtrafficsceneclassification AT soomoklee mosaicmixedattentionbasedunexpectedtrafficsceneclassification AT ilsooyun mosaicmixedattentionbasedunexpectedtrafficsceneclassification |