Most existing drone-based inspection systems require the drone to fly dangerously close to the target or follow complex flight paths to capture small details. In addition, drone flight is affected by disturbances and localization inaccuracies, which can cause the drone to lose sight of its supposed target when it has a narrow view. Furthermore, trajectory planning often requires prior information about the target's geometry, position, and orientation, which is not always available for non-structural targets such as trees, vehicles, or people. To address these challenges, this paper presents aerial_micro_inspection, a generic pipeline for aerial micro-inspection across different use cases. The pipeline assumes a PX4-powered drone equipped with two cameras: (i) a zoomed, gimbal-mounted inspection camera that captures fine details without requiring the drone to fly very close to the target, and (ii) a wide-field-of-view stereo navigation camera that acquires the target surface on site, estimates its range, and partitions it into smaller inspection regions. In addition, a vision-based feedback loop compensates for drone motion while the inspection camera visits small partitions of a larger surface. We evaluate the pipeline in simulation and real-world experiments, mainly in two use-case scenarios: tree inspection for detecting oak processionary caterpillars and their eggs, and greenhouse inspection of sticky traps for detecting whiteflies. The results show improved coverage robustness under drone disturbances in simulation, as well as effective detection of caterpillars and eggs and high-detail imaging of insects in real-world experiments. The pipeline is open-source, developed in ROS 2, and can be adapted to new applications by replacing the surface-segmentation and micro-target detection checkpoints. The code is available at: https://github.com/SaxionMechatronics/aerial_micro_inspection

翻译：现有的大多数基于无人机的检测系统要求无人机飞至危险地靠近目标或遵循复杂的飞行路径以捕捉微小细节。此外，无人机飞行受扰动和定位不精确的影响，狭窄视野下可能导致无人机丢失预期目标。再者，轨迹规划通常需要关于目标几何形状、位置和姿态的先验信息，而这对于树木、车辆或人等非结构目标并不总是可用。为解决这些挑战，本文提出一种通用的空中微观检测管线（aerial_micro_inspection），适用于不同用例场景。该管线假设搭载PX4飞控的无人机配备双相机：（i）安装于云台上的变焦检测相机，无需无人机贴近目标即可捕捉精细细节；（ii）大视场立体导航相机，用于现场采集目标表面、估算其距离并分割为更小的检测区域。此外，基于视觉的反馈回路可在检测相机遍历大表面上的小分区时补偿无人机运动。我们通过仿真和真实实验评估该管线，主要应用于两个场景：检测橡树丛中橡树行军虫及其虫卵的树冠检测，以及检测温室内粘虫板上粉虱的温室检测。仿真结果展示了在无人机扰动下覆盖鲁棒性的提升，真实实验则实现了对幼虫和虫卵的有效检测以及昆虫的高细节成像。该管线基于ROS 2开发并开源，可通过替换表面分割与微观目标检测检查点来适配新应用。代码地址：https://github.com/SaxionMechatronics/aerial_micro_inspection