Spotting shipping container codes with deep learning methods
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Zhang, Ran
issued: 2022-12-12T17:32:41Z
issued: 2022
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: Automated container code reading systems are of great value to logistics management. However, previous container code spotting methods have limited recognition performance in complex outdoor environments of transportation industry. Therefore, this research focuses on designing high-performance shipping container code spotting (including detection and recognition) methods for automated container code reading. First, an adaptive deep learning framework is proposed for horizontal code spotting. Adaptive threshold and average score aggregation (ASA) are designed to remove the noisy text. The detected code regions are adaptively adjusted for code recognition in our framework. Experiments have been conducted on real horizontal code image datasets. The overall recognition accuracy has been improved from 90.17% to 93.33%. Second, a deep learning model is proposed for vertical code spotting. It aligns vertical features of container codes. In addition, it includes two recognition pipelines and an uncertainty and probability-based decision strategy to adaptively choose the final results from two pipelines. Experiments conducted on real vertical code datasets demonstrate that the overall recognition f1-score performance has been improved from 93.5% to 96.5%. Third, a visual and textual information fusion-based zero-shot deep learning framework is proposed to spot placard codes. In this framework, Logarithmic Weighted Cross-Entropy (LWCE) is designed to reduce the effects of imbalanced classes and Logarithmic Weighted Confidence Fusion (LWCF) is proposed to fuse the visual and textual information to handle unseen classes and enhance performance in seen classes. Our proposed framework has been validated on real industrial placard datasets. The overall end-to-end recognition f1-score performance has been improved from 79.97% to 92.77%. Overall, our deep learning-based code spotting methods improve the accuracy of automated container code recognition and facilitate automated contai…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/83394
note: Spotting Shipping Container Codes withDeep Learning MethodsbyRan ZhangB.Eng., Xidian University, 2015M.Eng., Capital Normal University, 2018a thesis submitted in partial fulfillmentof the requirements for the degree ofDOCTOR OF PHILOSOPHYinthe college of graduate studies(Electrical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)December 2022© Ran Zhang, 2022The following individuals certify that they have read, and recommend to theCollege of Graduate Studies for acceptance, the dissertation entitled:SPOTTING SHIPPING CONTAINER CODES WITH DEEP LEARNINGMETHODSsubmitted by Ran Zhang in partial fulfillment of the requirements of thedegree of Doctor of Philosophy.Dr. Zheng Liu, School of Engineering, The University of British ColumbiaSupervisorDr. Richard Klukas, School of Engineering, The University of British ColumbiaSupervisory Committee MemberDr. Lo¨ıc Markley, School of Engineering, The University of British ColumbiaSupervisory Committee MemberDr. Jian Liu, School of Engineering, The University of British ColumbiaUniversity ExaminerDr. Sreeraman Rajan, Department of Systems and Computer Engineering,Carleton UniversityExternal ExamineriiABSTRACTAbstractAutomated container code reading systems are of great value to logisticsmanagement. However, previous container code spotting methods have lim-ited recognition performance in complex outdoor environments of transporta-tion industry. Therefore, this research focuses on designing high-performanceshipping container code spotting (including detection and recognition) meth-ods for automated container code reading.First, an adaptive deep learning framework is proposed for horizontalcode spotting. Adaptive threshold and average score aggregation (ASA) aredesigned to remove the noisy text. The detected code regions are adaptivelyadjusted for code recognition in our framework. Experiments have beenconducted on real horizontal code image datasets. The overall recognitionaccuracy has been improved from 90.17% to 93.33%.S…
has type: Thesis/Dissertation
date issued: 2023-02
is shown at: 10.14288/1.0422494
language: eng
degree discipline: Electrical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Liu, Zheng (Professor of engineering)
type: Text
type: Still Image
identifier URI: http://hdl.handle.net/2429/83394