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Confidence-Guided Code Recognition for Shipping Containers Using Deep Learning

Đã lưu trong:
Xuất bản năm:Big Data and Cognitive Computing vol. 9, no. 12 (2025), p. 316-339
Tác giả chính: Hlabisa Sanele
Tác giả khác: Khuboni Ray Leroy, Jules-Raymond, Tapamo
Được phát hành:
MDPI AG
Những chủ đề:
Datasets
Accuracy
Ports
Deep learning
Cargo containers
Fonts
Computer terminals
Artificial neural networks
Transportation networks
Radio frequency identification
Computer vision
Image quality
Object recognition
Localization
Illumination
Cost effectiveness
Shipping
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Bài tóm tắt:Shipping containers are vital to the transportation industry due to their cost-effectiveness and compatibility with intermodal systems. With the significant increase in container usage since the mid-20th century, manual tracking at port terminals has become inefficient and prone to errors. Recent advancements in Deep Learning for object detection have introduced Computer Vision as a solution for automating this process. However, challenges such as low-quality images, varying font sizes & illumination, and environmental conditions hinder recognition accuracy. This study explores various architectures and proposes a Container Code Localization Network (CCLN), utilizing ResNet and UNet for code identification, and a Container Code Recognition Network (CCRN), which combines Convolutional Neural Networks with Long Short-Term Memory to convert the image text into a machine-readable format. By enhancing existing shipping container localization and recognition datasets with additional images, our models exhibited improved generalization capabilities on other datasets, such as Syntext, for text recognition. Experimental results demonstrate that our system achieves <inline-formula>97.93%</inline-formula> accuracy at <inline-formula>64.11</inline-formula> frames per second under challenging conditions such as varying font sizes, illumination, tilt, and depth, effectively simulating real port terminal environments. The proposed solution promises to enhance workflow efficiency and productivity in container handling processes, making it highly applicable in modern port operations.
số ISSN:2504-2289
DOI:10.3390/bdcc9120316
Nguồn:Advanced Technologies & Aerospace Database