Date of Award

9-27-2023

Publication Type

Thesis

Degree Name

M.Sc.

Department

Computer Science

Keywords

Computer Vision;Convolutional Neural Network;Deep Learning;Image Annotation;Machine Learning;Object Detection

Supervisor

Dan Wu

Supervisor

Ziad Kobti

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

This thesis presents a new end-to-end hybrid machine learning (ML) and deep learning (DL) approach for semi-automatic image annotation (SAIA) and automatic image annotation (AIA) in industrial assembly line setups. Image annotation refers to adding descriptive labels or tags to an image to provide information about the objects and features present in the image. On a high level, the proposed system uses the following steps to annotate images. The first step involves using an ML algorithm, Haar cascade, to split an image into smaller regions of interest (ROI) based on the object of interest, in our case, the connectors of a car’s wire harness (WH). The second step involves using a DL model, specifically a convolutional neural network (CNN), to classify the ROI images. Our proposed system can do this work both semi- automatically and automatically. In SAIA, the users can specify which connectors they want to annotate by providing only the class label of the connector(s) and in AIA, the users will only provide an unannotated image for annotation. This work is unique as no other work was set up in the same industrial setting as ours, producing outstanding results surpassing other state-of-the-art image annotation models. The custom hybrid ML and DL framework, as well as building a unique custom dataset related to the automobile industry assembly line, are significant contributions of this work.

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