EvDetMAV: Generalized MAV Detection from Moving Event Cameras

TL;DR

This paper introduces a novel event-based MAV detection method that leverages propeller features in event streams, outperforming existing RGB-based approaches and providing a new dataset for the community.

Contribution

It presents a new event-based MAV detection approach, introduces the first MAV dataset for event cameras, and demonstrates significant performance improvements without training.

Findings

Achieves 83.0% precision and 81.5% recall on the new dataset.

Outperforms state-of-the-art methods significantly.

Effective in challenging scenarios with high-speed MAVs.

Abstract

Existing micro aerial vehicle (MAV) detection methods mainly rely on the target's appearance features in RGB images, whose diversity makes it difficult to achieve generalized MAV detection. We notice that different types of MAVs share the same distinctive features in event streams due to their high-speed rotating propellers, which are hard to see in RGB images. This paper studies how to detect different types of MAVs from an event camera by fully exploiting the features of propellers in the original event stream. The proposed method consists of three modules to extract the salient and spatio-temporal features of the propellers while filtering out noise from background objects and camera motion. Since there are no existing event-based MAV datasets, we introduce a novel MAV dataset for the community. This is the first event-based MAV dataset comprising multiple scenarios and different types of MAVs. Without training, our method significantly outperforms state-of-the-art methods and can deal with challenging scenarios, achieving a precision rate of 83.0\% (+30.3\%) and a recall rate of 81.5\% (+36.4\%) on the proposed testing dataset. The dataset and code are available at: https://github.com/WindyLab/EvDetMAV.