标题： UCorr：自主无人机的线检测和深度估计 显示英文标题

标题： UCorr: Wire Detection and Depth Estimation for Autonomous Drones

摘要： 在完全自主无人机领域，准确检测障碍物对于确保安全导航和防止碰撞至关重要。 在这些挑战中，电线的检测尤为突出，因为它们细长的外形带来了一个独特而复杂的问题。 为了解决这个问题，我们提出了一种创新的解决方案，即一种单目端到端模型，用于电线分割和深度估计。 我们的方法利用了在合成数据上训练的时间相关层，使模型能够有效处理电线检测和深度估计这一复杂的联合任务。 我们展示了所提出的方法在电线检测和深度估计联合任务中优于现有的竞争性方法。 我们的结果强调了我们的模型在提高自主无人机的安全性和精确性方面的潜力，并为其在现实场景中的应用前景提供了启示。 显示英文摘要

摘要： 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.