Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915446736814080 |
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| author | Liu, Zhanwen Sun, Yujing Wang, Yang Yang, Nan Li, Shengbo Eben Zhao, Xiangmo |
| author_facet | Liu, Zhanwen Sun, Yujing Wang, Yang Yang, Nan Li, Shengbo Eben Zhao, Xiangmo |
| contents | The dynamic range limitation of conventional RGB cameras reduces global contrast and causes loss of high-frequency details such as textures and edges in complex traffic environments (e.g., nighttime driving, tunnels), hindering discriminative feature extraction and degrading frame-based object detection. To address this, we integrate a bio-inspired event camera with an RGB camera to provide high dynamic range information and propose a motion cue fusion network (MCFNet), which achieves optimal spatiotemporal alignment and adaptive cross-modal feature fusion under challenging lighting. Specifically, an event correction module (ECM) temporally aligns asynchronous event streams with image frames via optical-flow-based warping, jointly optimized with the detection network to learn task-aware event representations. The event dynamic upsampling module (EDUM) enhances spatial resolution of event frames to match image structures, ensuring precise spatiotemporal alignment. The cross-modal mamba fusion module (CMM) uses adaptive feature fusion with a novel interlaced scanning mechanism, effectively integrating complementary information for robust detection. Experiments conducted on the DSEC-Det and PKU-DAVIS-SOD datasets demonstrate that MCFNet significantly outperforms existing methods in various poor lighting and fast moving traffic scenarios. Notably, on the DSEC-Det dataset, MCFNet achieves a remarkable improvement, surpassing the best existing methods by 7.4% in mAP50 and 1.7% in mAP metrics, respectively. The code is available at https://github.com/Charm11492/MCFNet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10704 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios Liu, Zhanwen Sun, Yujing Wang, Yang Yang, Nan Li, Shengbo Eben Zhao, Xiangmo Computer Vision and Pattern Recognition The dynamic range limitation of conventional RGB cameras reduces global contrast and causes loss of high-frequency details such as textures and edges in complex traffic environments (e.g., nighttime driving, tunnels), hindering discriminative feature extraction and degrading frame-based object detection. To address this, we integrate a bio-inspired event camera with an RGB camera to provide high dynamic range information and propose a motion cue fusion network (MCFNet), which achieves optimal spatiotemporal alignment and adaptive cross-modal feature fusion under challenging lighting. Specifically, an event correction module (ECM) temporally aligns asynchronous event streams with image frames via optical-flow-based warping, jointly optimized with the detection network to learn task-aware event representations. The event dynamic upsampling module (EDUM) enhances spatial resolution of event frames to match image structures, ensuring precise spatiotemporal alignment. The cross-modal mamba fusion module (CMM) uses adaptive feature fusion with a novel interlaced scanning mechanism, effectively integrating complementary information for robust detection. Experiments conducted on the DSEC-Det and PKU-DAVIS-SOD datasets demonstrate that MCFNet significantly outperforms existing methods in various poor lighting and fast moving traffic scenarios. Notably, on the DSEC-Det dataset, MCFNet achieves a remarkable improvement, surpassing the best existing methods by 7.4% in mAP50 and 1.7% in mAP metrics, respectively. The code is available at https://github.com/Charm11492/MCFNet. |
| title | Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.10704 |