Dents on the aircraft skin are frequent and may easily go undetected during airworthiness checks, as their inspection process is tedious and extremely subject to human factors and environmental conditions. Nowadays, 3D scanning technologies are being proposed for more reliable, human-independent measurements, yet the process of inspection and reporting remains laborious and time consuming because data acquisition and validation are still carried out by the engineer. For full automation of dent inspection, the acquired point cloud data must be analysed via a reliable segmentation algorithm, releasing humans from the search and evaluation of damage. This paper reports on two developments towards automated dent inspection. The first is a method to generate a synthetic dataset of dented surfaces to train a fully convolutional neural network. The training of machine learning algorithms needs a substantial volume of dent data, which is not readily available. Dents are thus simulated in random positions and shapes, within criteria and definitions of a Boeing 737 structural repair manual. The noise distribution from the scanning apparatus is then added to reflect the complete process of 3D point acquisition on the training. The second proposition is a surface fitting strategy to convert 3D point clouds to 2.5D. This allows higher resolution point clouds to be processed with a small amount of memory compared with state-of-the-art methods involving 3D sampling approaches. Simulations with available ground truth data show that the proposed technique reaches an intersection-over-union of over 80%. Experiments over dent samples prove an effective detection of dents with a speed of over 500 000 points per second.

翻译：飞机蒙皮上的凹痕频繁出现，且在适航检查过程中极易被忽略，因其检测工作繁琐且高度受人为因素与环境条件影响。当前，3D扫描技术被提出用于更可靠、去人化的测量，然而检测与报告流程仍耗时费力，因为数据采集与验证仍需工程师手动完成。为实现凹痕检测的全自动化，必须通过可靠的分割算法对采集的点云数据进行分析，从而将人从损伤搜索与评估中解放出来。本文报道了面向自动化凹痕检测的两项进展。第一项是生成凹痕表面合成数据集以训练全卷积神经网络的方法。机器学习算法的训练需要大量凹痕数据，而此类数据难以直接获取。因此，我们在波音737结构维修手册的标准与定义范围内，在随机位置和形状下模拟凹痕。随后加入扫描设备的噪声分布，以反映训练过程中3D点采集的完整流程。第二项提案是一种将3D点云转换为2.5D的曲面拟合策略。与涉及3D采样方法的现有技术相比，该方法能够在较小的内存占用下处理更高分辨率的点云。基于已知真实数据的仿真表明，所提技术达到超过80%的交并比。在凹痕样本上的实验证明，该方法能以每秒超过50万点的速度有效检测凹痕。