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:飞机皮肤上的牙科是经常的,而且很容易在适航检查期间不被发现,因为其检查过程是枯燥的,极受人的因素和环境条件的影响。现在,正在提议3D扫描技术,以进行更可靠、人独立的测量,然而,检查和报告程序仍然费时费力和费时,因为数据获取和验证仍然由工程师进行。要完全自动化点检查,就必须通过可靠的分解算法分析获得的点云数据,使人类从搜索和评估损害中解脱出来。本文报告了自动口腔检查的两个发展动态。第一个是生成一个精密表面的合成数据集,以训练一个完全革命性神经网络。对机器学习算法的培训需要大量不易获得的缩记数据。因此,根据波音737结构修理手册的标准和定义,对牙科进行随机位置和形状模拟。然后将扫描器的噪音分布添加来反映培训中3D点收购的完整过程。第二个提议是用一个小的表面精度的精度探测方法来生成精度的精度表层数据,将三D的精度的精度的精度的精度数据转换为2.5分辨率的模度。</s>