Oct 19, 2026J/K move · Enter open · S save
Peijun Shi, Jiannan Zhao, Xiujin Zhu, Zhenguo Lao+2
Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603 Lembah Pantai, Kuala Lumpur, Malaysia. · School of Electrical Engineering, Guangxi University, Nanning 530004, China · Computational Intelligence Laboratory, School of Computer Science, University of Lincoln, Lincoln LN6 7TS, U.K.
Autonomous vehicles must detect objects faster than a frame camera delivers images. Event cameras record brightness changes with microsecond resolution, so RGB--event detectors can decide between frames, at an operating frequency, the rate of decisions, far above the frame rate. As this rate rises, each decision receives a sparser event window, and the reliability of the events relative to the frame varies over the scene and over time. Existing detectors weight the sensors per sample and encode the frame at the reduced event resolution. Reliability-supervised routing assigns a fusion weight to every location and supervises it with targets from the losses of two single-modality classifiers used only in training, moving loss-based supervision of fusion weights from whole samples to individual locations. Asymmetric-resolution fusion lets the frame carry the decision where the events fail: it encodes the frame at native resolution and pools every feature level onto the event grid, reusing all pretrained weights. On the DSEC-Det dataset, routing halves the accuracy loss from 20 to 180 Hz, from 6.3 to 3.1 mAP, and lowers it in every seed. The resulting detector, ERA-Det, sets a new state of the art at 63.2 mAP, 3.7 mAP above FlexEvent, leads at every operating frequency, and at 180 Hz is more accurate than FlexEvent at 20 Hz. On two DAVIS346 datasets, the router lowers the frame weight at night and in low light and raises it when the events vanish. ERA-Det upgrades released FlexEvent weights in 5,000 iterations on one GPU.