Countersink inspection is crucial in various automated assembly lines, especially in the aerospace and automotive sectors. Advancements in machine vision introduced automated robotic inspection of countersinks using laser scanners and monocular cameras. Nevertheless, the aforementioned sensing pipelines require the robot to pause on each hole for inspection due to high latency and measurement uncertainties with motion, leading to prolonged execution times of the inspection task. The neuromorphic vision sensor, on the other hand, has the potential to expedite the countersink inspection process, but the unorthodox output of the neuromorphic technology prohibits utilizing traditional image processing techniques. Therefore, novel event-based perception algorithms need to be introduced. We propose a countersink detection approach on the basis of event-based motion compensation and the mean-shift clustering principle. In addition, our framework presents a robust event-based circle detection algorithm to precisely estimate the depth of the countersink specimens. The proposed approach expedites the inspection process by a factor of 10$\times$ compared to conventional countersink inspection methods. The work in this paper was validated for over 50 trials on three countersink workpiece variants. The experimental results show that our method provides a precision of 0.025 mm for countersink depth inspection despite the low resolution of commercially available neuromorphic cameras.

翻译：埋头孔检测在各类自动化装配线中至关重要，特别是在航空航天和汽车领域。机器视觉技术的进步推动了基于激光扫描仪和单目摄像头的埋头孔自动化机器人检测。然而，上述传感管道要求机器人在每个孔位暂停检测，这是由于高延迟以及运动带来的测量不确定性，导致检测任务执行时间延长。相比之下，神经形态视觉传感器有潜力加快埋头孔检测过程，但神经形态技术的非传统输出使得传统图像处理技术无法直接应用。因此，需要引入新颖的基于事件的感知算法。我们提出了一种基于事件运动补偿和均值漂移聚类原理的埋头孔检测方法。此外，我们的框架还提出了一种鲁棒的基于事件的圆检测算法，以精确估计埋头孔试样的深度。与传统埋头孔检测方法相比，该方法将检测过程加速了10倍。本文的工作在三种埋头孔工件变体上进行了超过50次试验验证。实验结果表明，尽管商用神经形态相机分辨率较低，我们的方法在埋头孔深度检测上仍达到了0.025毫米的精度。