EfficientMFD: Towards More Efficient Multimodal Synchronous Fusion Detection

Jiaqing Zhang, Mingxiang Cao, Xue Yang, Weiying Xie, Jie Lei, Daixun Li, Geng Yang, Wenbo Huang, Yunsong Li
Computer Science, Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition (cs.CV)
2024-03-14 00:00:00
Multimodal image fusion and object detection play a vital role in autonomous driving. Current joint learning methods have made significant progress in the multimodal fusion detection task combining the texture detail and objective semantic information. However, the tedious training steps have limited its applications to wider real-world industrial deployment. To address this limitation, we propose a novel end-to-end multimodal fusion detection algorithm, named EfficientMFD, to simplify models that exhibit decent performance with only one training step. Synchronous joint optimization is utilized in an end-to-end manner between two components, thus not being affected by the local optimal solution of the individual task. Besides, a comprehensive optimization is established in the gradient matrix between the shared parameters for both tasks. It can converge to an optimal point with fusion detection weights. We extensively test it on several public datasets, demonstrating superior performance on not only visually appealing fusion but also favorable detection performance (e.g., 6.6% mAP50:95) over other state-of-the-art approaches.
PDF: EfficientMFD: Towards More Efficient Multimodal Synchronous Fusion Detection.pdf
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