Journal ArticleDOI
An Adaptive Deep Learning Framework for Shipping Container Code Localization and Recognition
TLDR
An adaptive deep learning framework for shipping container code localization and recognition and it is demonstrated that the proposed framework achieved better localization performance and obtained 93.33% recognition accuracy.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.read more
Citations
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Fault Diagnosis and Fault Frequency Determination of Permanent Magnet Synchronous Motor Based on Deep Learning
TL;DR: In this article, a one-dimensional convolutional neural network (CNN) was proposed for the diagnosis of permanent magnet synchronous motors, which consists of multiple CNN feature-extraction modules.
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Computational Logistics for Container Terminal Handling Systems with Deep Learning.
TL;DR: In this article, a deep learning model core computing architecture (DLM-CCA) for liner berthing time prediction is presented to practice container terminal-oriented neural-physical fusion computation (CTO-NPFC).
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An Attention Mechanism Oriented Hybrid CNN-RNN Deep Learning Architecture of Container Terminal Liner Handling Conditions Prediction
TL;DR: In this article, an attention mechanism oriented hybrid convolutional neural network and recurrent neural network deep learning architecture (AMO-HCR-DLA) is proposed to predict the container terminal liner handling conditions that mainly include liner handling time (LHT) and total working time of quay crane farm (TWT-QCF).
Journal ArticleDOI
Vehicle Tire Text Reader: Text Spotting and Rectifying for Small, Curved, and Rotated Characters
TL;DR: Wang et al. as discussed by the authors proposed a coarse-to-fine method of detecting and rectification for tire text code (TTC) to determine whether or not a tire is over the service life.
Journal ArticleDOI
The Impacts of the Applications of Artificial Intelligence in Maritime Logistics
TL;DR: In this article , the authors identify current approaches in the usage of Artificial Intelligence (AI) methods for solving shipping problems and present a comprehensive assessment, which highlights research gaps and forecasts future research orientations.
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Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
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