Open Collections

Zhang, Ran

University of British Columbia

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 container management.

Text; Still Image

2022-12-12

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/83394

Electrical Engineering

University of British Columbia

UBCO

http://creativecommons.org/licenses/by-nc-nd/4.0/