Deep learning methods applied to anomaly detection in vehicle manufacturing and operations

As one of the most common modes of transportation, vehicles are very closely related to our lives. As a result, safety is an important issue in both vehicle production process and vehicle operations. Recently, unmanned vehicles have received much attention from both companies and academia. The first thing we need to consider for unmanned vehicles is safety. With the adoption of deep learning (DL) methods, DL-based defect detection and fault detection technology has evolved into a powerful tool with increased accuracy and autonomy compared with traditional detection technology. This thesis presents novel deep learning methods which can help detect defects in X-ray images of vehicle engines during the manufacturing process and various faults in the operation of autonomous vehicles. This thesis is focused on applying deep neural networks in image-based classification operations. The results show that the algorithms can successfully detect the anomalies with satisfactory accuracy.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Ren, Rui

Advisors dc:contributor.advisor

• Green, Mark
• Ren, Jing

• Artificial intelligence
• Deep learning
• Defect detection
• Deep neural networks (DNN)
• Autoencoder

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1071

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1071