Paper Info
Title
An Adaptive Deep Learning Framework For Shipping Container Code Localization And Recognition
Abstract
Shipping containers play an important role in global transportation. As container codes are the unique identifiers for shipping containers, recognizing these codes is an essential step to manage the containers and logistics. The conventional code localization methods can easily be interfered by varied noises and cannot identify the best regions for code recognition. In this article, we propose an adaptive deep learning framework for shipping container code localization and recognition. In the framework, the noisy text regions will be removed by an adaptive score aggregation (ASA) algorithm. The code region boundaries are identified by the average-to-maximum suppression range (AMSR) algorithm. Thus, the predicted locations can be adjusted within this range to fit the code recognition model to achieve higher accuracy. The experimental results on the comparative study with the state-of-the-art models, including EAST, PSENet, GCRNN, and MaskTextSpotter, demonstrated that the proposed framework achieved better localization performance and obtained 93.33% recognition accuracy. The processing speed reaches 1.13 frames/s, which is sufficient to meet the operational requirements. Thus, the proposed solution will facilitate the digital transformation of shipping container management and logistics at ports.
Year
DOI
Venue
2021
10.1109/TIM.2020.3016108
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Keywords
DocType
Volume
Adaptive container code localization and recognition, convolutional neural network (CNN), deep learning, end-to-end text recognition, recurrent neural network (RNN), text detection
Journal
70
ISSN
Citations 
PageRank 
0018-9456
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Ran Zhang13313.46
Zhila Bahrami200.34
Teng Wang301.69
Zheng Liu433939.14