Fast Drone Detection With Optimized Feature Capture and Modeling Algorithms

Abstract

Detecting drones is a complex challenge, primarily due to their small feature size for extraction and variable lighting conditions. It is crucial to effectively capture and model features for drone detection. To accurately detect drones, we propose feature capture and modeling modules. The feature capture module has a minimal number of FLOPs and parameters. It consists of both local and global attention branches, which capture contextual information and global dependencies across the entire feature set. Complementing this, our feature modeling module innovatively calculates attention maps without any additional parameters. This module augments the capability of the feature capture mechanism to represent complex patterns more effectively. Finally, to ensure rapid deployment, we convert the proposed models to machine codes by introducing a compiler, accelerating drone detection. The compiler unifies inter- and intra-operator scheduling with task abstraction. It optimizes the codes for hardware. In compiling time, the effective schedule is performed. The compilation ensures that drone detection is real-time and accuracy remains unchanged. Through rigorous testing, our methods have demonstrated superiority over most current ones in several metrics, including accuracy, parameter quantities, FLOPs, average FPS, visual effects, and latency. Our method yields at least 23.5% and 12.47% higher $AR_{M}$ than existing methods on DUT-Anti-UAV and Online Drone datasets. Our inference speed is at least 6.49% higher than other methods on NVIDIA RTX 2080 Ti GPU.

Keywords

• Deep neural networks (DNNs)
• drone detection
• feature capture module (FCM)
• feature modeling module (FMM)
• optimization