Y. Chung and K. Kim, Automated visual inspection system of automobile doors and windows using the adaptive feature extraction, Second Int. Conf. Knowl-Based Intell. Electron. Syst. Proc, 1998.

M. Ichitsubo, I. Horiguchi, and M. Nagamachi, A study on the evaluation of structure damages for the visual inspection, Proc. Int. Conf. Active Media Technol, 2005.

C. Cho, B. Chung, and M. Park, Development of real-time vision-based fabric inspection system, IEEE Trans. Ind. Electron, vol.52, issue.4, pp.1073-1079, 2005.

T. Brosnan and D. Sun, Improving quality inspection of food products by computer vision -a review, J. Food Eng, vol.61, issue.1, pp.3-16, 2004.

W. Wu, M. Wang, and C. Liu, Automated inspection of printed circuit boards through machine vision, Comput. Ind, vol.28, issue.2, pp.103-111, 1996.

C. Lu and D. Tsai, Automatic defect inspection for LCDs using singular value decomposition, Int. J. Adv. Manuf. Technol, vol.25, issue.1-2, pp.53-61, 2005.

D. Tsai and S. Lai, Defect detection in periodically patterned surfaces using independent component analysis, Pattern Recognit, vol.41, issue.9, pp.2812-2832, 2008.

H. B. Abdallah, Automatic inspection of aeronautical mechanical assemblies by matching the 3D CAD model and real 2D images, J. Imaging, vol.5, pp.81-108, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02320900

I. Jovancevic, 3D point cloud analysis for detection and characterization of defects on airplane exterior surface, J. Nondestr. Eval, vol.36, p.74, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01622056

I. Jovancevic, Automated exterior inspection of an aircraft with a pan-tilt-zoom camera mounted on a mobile robot, J. Electron. Imaging, vol.24, p.61110, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01351008

H. B. Abdallah, 3D point cloud analysis for automatic inspection of aeronautical mechanical assemblies, Proc. SPIE 11172, p.111720, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02087366

P. Je-kang, Machine learning-based imaging system for surface defect inspection, Int. J. Precis. Eng. Manuf, vol.3, pp.303-310, 2016.

S. Ravikumar, K. Ramachandran, and V. Sugumaran, Machine learning approach for automated visual inspection of machine components, Expert Syst. Appl, vol.38, issue.14, pp.3260-3266, 2011.

D. Guifang, A machine learning-based framework for automatic visual inspection of microdrill bits in PCB production, IEEE Trans. Syst. Man Cybern, vol.42, issue.6, pp.1679-1689, 2012.

V. Hoskere, Vision-based structural inspection using multiscale deep convolutional neural networks, Third Huixian Int, 2017.

T. Xian, Automatic metallic surface defect detection and recognition with convolutional neural networks, Appl. Sci, vol.8, issue.9, p.1575, 2018.

Y. Ruifang, Intelligent defect classification system based on deep learning, Adv. Mech. Eng, vol.29, issue.3, p.168781401876668, 2018.

E. Oumayma, L. Hamid, and S. Chafik, Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks, PLoS One, vol.13, issue.11, p.203192, 2018.

L. Yiting, Research on a surface defect detection algorithm based on MobileNet-SSD, Appl. Sci, vol.8, issue.9, p.1678, 2018.

C. Young-jin, Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types, Comput.-Aided Civ. Infrastruct. Eng, vol.33, issue.9, pp.731-747, 2017.

D. Weimer, A. Y. Benggolo, and M. Freitag, Context-aware deep convolutional neural networks for industrial inspection, Australas. Conf. Artif. Intell, 2015.

N. Fusaomi, Design tool of deep convolutional neural network for intelligent visual inspection, IOP Conf. Ser. Mater. Sci. Eng, vol.423, issue.1, p.123, 2018.

B. Vogel-heuser, Evolution of software in automated production systems: challenges and research directions, J. Syst. Software, vol.110, pp.54-84, 2015.

S. V. Khedaskar, Survey of image processing and identification techniques, J. Res. Innov, vol.1, issue.1, pp.1-10, 2018.

R. S. Hegadi, Image processing: research opportunities and challenges, Natl. Seminar Res. Comput, 2010.

V. Zharkova, Feature recognition in solar image, Artif. Intell. Rev, vol.23, issue.3, pp.209-266, 2005.

A. M. Kim, R. C. Olsen, and F. A. Kruse, Methods for lidar point cloud classification using local neighborhood statistics, Proc. SPIE, vol.8731, p.873103, 2013.

M. Weinmann, Contextual classification of point cloud data by exploiting individual 3D neighbourhoods, ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci, pp.271-278, 2015.

M. Weinmann, B. Jutzi, and C. Mallet, Geometric features and their relevance for 3D point cloud classification, ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. IV-1/W1, issue.1, pp.157-164, 2017.
URL : https://hal.archives-ouvertes.fr/hal-02384446

Z. Heng, Z. Bin, and L. Yanli, Object classification based on 3D point clouds covariance descriptor, IEEE Int. Conf. Comput. Sci. and Eng. and IEEE Int. Conf. Embedded and Ubiquitous Comput, 2017.

M. Omidalizarandi and M. Saadatseresht, Segmentation and classification of point clouds from dense aerial image matching, Int. J. Multimedia Appl, vol.5, issue.4, pp.4145-4169, 2013.

C. Becker, Classification of aerial photogrammetric 3D point clouds, ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci, vol.84, issue.5, pp.287-295, 2018.

M. Zhenga, M. Lemmens, and P. Van-oosterom, Classification of mobile laser scanning point clouds from height features, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, issue.7, pp.321-325, 2017.

X. Binbin, Segmentation-based classification for 3D point clouds in the road environment, Int. J. Remote Sens, vol.39, pp.6182-6212, 2018.

Y. Zegaoui, Urban object classification with 3D deep-learning, Joint Urban Remote Sens. Event, 2019.
URL : https://hal.archives-ouvertes.fr/lirmm-02087761

Z. Ruibin, P. Mingyong, and W. Jidong, Classifying airborne lidar point clouds via deep features learned by a multi-scale convolutional neural network, Int. J. Geogr. Inf. Sci, vol.32, issue.5, pp.960-979, 2018.

X. Roynard, J. Deschaud, and F. Goulette, Classification of point cloud for road scene understanding with multiscale voxel deep network, 10th Workshop Plann, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01763469

Z. Wu, 3D ShapeNets: a deep representation for volumetric shapes, IEEE Conf. Comput. Vision and Pattern Recognit, 2015.

C. R. Qi, Deep learning on point sets for 3D classification and segmentation, IEEE Conf. Comput. Vision and Pattern Recognit, 2017.

P. Cignoni, MeshLab: an open-source mesh processing tool, 2008.

, Mechanics and Optics (Russia) with a bachelor's degree in infocommunication technologies and systems. He received his master's degree in computer vision in 2019 from the University of Burgundy (France). Currently, he is conducting his PhD at SurgAR (Clermont-Ferrand, France) in partnership with the, His work is primarily focused on computer vision and machine learning applications for surgical augmented reality

, He graduated in 2011 from joint Erasmus Mundus Master program in Computer Vision and Robotics (VIBOT) conducted by University of Burgundy, 2008 from Faculty of Natural Science and Mathematics at the University of Montenegro with a mathematics degree (speciality computer science)

, Nour Islam Mokhtari graduated in 2018 from the University of Burgundy with a master's degree in computer vision. He also holds an engineering degree from Polytechnic School of Algiers (Ecole Nationale Polytechnique) in the field of control and automation. Currently, he is working at Diotasoft in Toulouse, France, as a research and development engineer, focusing on computer vision and machine learning applications for industrial visual inspection

J. Orteu, Grande Ecole" specialized in process engineering. He carries out his research work in the Institut Clément Ader laboratory (200 people). For more than 15 years, he has developed computer vision-based solutions for 3-D measurements in experimental mechanics (photomechanics) and for a few years he is more specifically