Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection

• URL: http://arxiv.org/abs/2407.12582v2
• Date: Thu, 31 Oct 2024 14:37:42 GMT
• Title: Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection
• Authors: Hu Cao, Zehua Zhang, Yan Xia, Xinyi Li, Jiahao Xia, Guang Chen, Alois Knoll,
• Abstract summary: Event cameras output sparse and asynchronous events, providing a potential solution to solve these problems. We propose a novel hierarchical feature refinement network for event-frame fusion. Our method exhibits significantly better robustness when introducing 15 different corruption types to the frame images.
• Score: 17.406051477690134
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In frame-based vision, object detection faces substantial performance degradation under challenging conditions due to the limited sensing capability of conventional cameras. Event cameras output sparse and asynchronous events, providing a potential solution to solve these problems. However, effectively fusing two heterogeneous modalities remains an open issue. In this work, we propose a novel hierarchical feature refinement network for event-frame fusion. The core concept is the design of the coarse-to-fine fusion module, denoted as the cross-modality adaptive feature refinement (CAFR) module. In the initial phase, the bidirectional cross-modality interaction (BCI) part facilitates information bridging from two distinct sources. Subsequently, the features are further refined by aligning the channel-level mean and variance in the two-fold adaptive feature refinement (TAFR) part. We conducted extensive experiments on two benchmarks: the low-resolution PKU-DDD17-Car dataset and the high-resolution DSEC dataset. Experimental results show that our method surpasses the state-of-the-art by an impressive margin of $\textbf{8.0}\%$ on the DSEC dataset. Besides, our method exhibits significantly better robustness (\textbf{69.5}\% versus \textbf{38.7}\%) when introducing 15 different corruption types to the frame images. The code can be found at the link (https://github.com/HuCaoFighting/FRN).

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