Next Article in Journal
Modelling and Resolution of Dynamic Reliability Problems by the Coupling of Simulink and the Stochastic Hybrid Fault Tree Object Oriented (SHyFTOO) Library
Previous Article in Journal
Factors Influencing Online Hotel Booking: Extending UTAUT2 with Age, Gender, and Experience as Moderators
Open AccessArticle

A Novel Approach to Component Assembly Inspection Based on Mask R-CNN and Support Vector Machines

by 1, 1,* and 1,2
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan 32003, Taiwan
Author to whom correspondence should be addressed.
Information 2019, 10(9), 282;
Received: 26 July 2019 / Revised: 20 August 2019 / Accepted: 28 August 2019 / Published: 11 September 2019
(This article belongs to the Special Issue IoT Applications and Industry 4.0)
Assembly is a very important manufacturing process in the age of Industry 4.0. Aimed at the problems of part identification and assembly inspection in industrial production, this paper proposes a method of assembly inspection based on machine vision and a deep neural network. First, the image acquisition platform is built to collect the part and assembly images. We use the Mask R-CNN model to identify and segment the shape from each part image, and to obtain the part category and position coordinates in the image. Then, according to the image segmentation results, the area, perimeter, circularity, and Hu invariant moment of the contour are extracted to form the feature vector. Finally, the SVM classification model is constructed to identify the assembly defects, with a classification accuracy rate of over 86.5%. The accuracy of the method is verified by constructing an experimental platform. The results show that the method effectively completes the identification of missing and misaligned parts in the assembly, and has good robustness. View Full-Text
Keywords: machine vision; Mask R-CNN; assembly detection; classification machine vision; Mask R-CNN; assembly detection; classification
Show Figures

Figure 1

MDPI and ACS Style

Huang, H.; Wei, Z.; Yao, L. A Novel Approach to Component Assembly Inspection Based on Mask R-CNN and Support Vector Machines. Information 2019, 10, 282.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

Back to TopTop