单目模型同时检测电线并估计深度，提升无人机避障能力
UCorr: Wire Detection and Depth Estimation for Autonomous Drones
- 设计端到端单目模型，引入时序相关层增强电线感知
- 在合成数据上训练后，电线检测与深度估计性能更优
- 适用于真实场景下无人机安全飞行，尤其适合电力巡检
在全自主无人机领域，障碍物的精准检测对保障安全航行、避免碰撞至关重要。其中，电线因其细长特性构成了独特而复杂的挑战。为此，我们提出一种基于单目图像的端到端模型，实现电线分割与深度估计的联合任务。该方法通过在合成数据上训练的时序相关层，赋予模型有效处理复杂联合任务的能力。实验表明，所提方法在电线检测与深度估计的联合任务中优于现有主流方法。结果证明，该模型具有提升无人机安全性与导航精度的潜力，展现出在真实场景中的广阔应用前景。
原文摘要 · Abstract (English)
In the realm of fully autonomous drones, the accurate detection of obstacles is paramount to ensure safe navigation and prevent collisions. Among these challenges, the detection of wires stands out due to their slender profile, which poses a unique and intricate problem. To address this issue, we present an innovative solution in the form of a monocular end-to-end model for wire segmentation and depth estimation. Our approach leverages a temporal correlation layer trained on synthetic data, providing the model with the ability to effectively tackle the complex joint task of wire detection and depth estimation. We demonstrate the superiority of our proposed method over existing competitive approaches in the joint task of wire detection and depth estimation. Our results underscore the potential of our model to enhance the safety and precision of autonomous drones, shedding light on its promising applications in real-world scenarios.
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