Confidence-Guided Code Recognition for Shipping Containers Using Deep Learning
में बचाया:
| में प्रकाशित: | Big Data and Cognitive Computing vol. 9, no. 12 (2025), p. 316-339 |
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| मुख्य लेखक: | |
| अन्य लेखक: | , |
| प्रकाशित: |
MDPI AG
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| विषय: | |
| ऑनलाइन पहुंच: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| टैग: |
कोई टैग नहीं, इस रिकॉर्ड को टैग करने वाले पहले व्यक्ति बनें!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3286257472 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2504-2289 | ||
| 024 | 7 | |a 10.3390/bdcc9120316 |2 doi | |
| 035 | |a 3286257472 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Hlabisa Sanele | |
| 245 | 1 | |a Confidence-Guided Code Recognition for Shipping Containers Using Deep Learning | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Datasets | ||
| 653 | |a Accuracy | ||
| 653 | |a Ports | ||
| 653 | |a Deep learning | ||
| 653 | |a Cargo containers | ||
| 653 | |a Fonts | ||
| 653 | |a Computer terminals | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Transportation networks | ||
| 653 | |a Radio frequency identification | ||
| 653 | |a Computer vision | ||
| 653 | |a Image quality | ||
| 653 | |a Object recognition | ||
| 653 | |a Localization | ||
| 653 | |a Illumination | ||
| 653 | |a Cost effectiveness | ||
| 653 | |a Shipping | ||
| 700 | 1 | |a Khuboni Ray Leroy | |
| 700 | 1 | |a Jules-Raymond, Tapamo | |
| 773 | 0 | |t Big Data and Cognitive Computing |g vol. 9, no. 12 (2025), p. 316-339 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286257472/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286257472/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286257472/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |