Deep learning–assisted real-time container corner casting recognition
Intelligent automated crane systems are now an integral part of container port automation. Accurate corner casting detection boosts the performance of an automated crane system which ultimately automates ships loading and unloading. Existing techniques use various traditional laser-based and vision-...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147718824462 |
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| _version_ | 1850174044144402432 |
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| author | Jaecheul Lee |
| author_facet | Jaecheul Lee |
| author_sort | Jaecheul Lee |
| collection | DOAJ |
| description | Intelligent automated crane systems are now an integral part of container port automation. Accurate corner casting detection boosts the performance of an automated crane system which ultimately automates ships loading and unloading. Existing techniques use various traditional laser-based and vision-based methods for corner casting detection. Challenging weather conditions, varying lighting conditions, light reflections from ground, and container rusting conditions are the main problems that affect the performance of automated cranes. From this line of research, we propose an end-to-end method that takes a low-quality video input and produces bounding boxes around corner castings by applying a recurrent neural network along with long short-term memory units. The expressive image features from GoogLeNet are used to produce intermediate image representations that are further tuned for our system. The proposed system uses back-propagation to allow joint tuning of all components. At least, four cameras are mounted on each crane and input stream is combined into a single image to reduce the computational cost. The proposed system outperforms all existing methods in terms of precision, recall, and F -measure. The proposed method is implemented in a real-time port and produces more than 98% accuracy in all conditions. |
| format | Article |
| id | doaj-art-c72c8edc9da44e4ab8d585197eb565b9 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-c72c8edc9da44e4ab8d585197eb565b92025-08-20T02:19:44ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-01-011510.1177/1550147718824462Deep learning–assisted real-time container corner casting recognitionJaecheul LeeIntelligent automated crane systems are now an integral part of container port automation. Accurate corner casting detection boosts the performance of an automated crane system which ultimately automates ships loading and unloading. Existing techniques use various traditional laser-based and vision-based methods for corner casting detection. Challenging weather conditions, varying lighting conditions, light reflections from ground, and container rusting conditions are the main problems that affect the performance of automated cranes. From this line of research, we propose an end-to-end method that takes a low-quality video input and produces bounding boxes around corner castings by applying a recurrent neural network along with long short-term memory units. The expressive image features from GoogLeNet are used to produce intermediate image representations that are further tuned for our system. The proposed system uses back-propagation to allow joint tuning of all components. At least, four cameras are mounted on each crane and input stream is combined into a single image to reduce the computational cost. The proposed system outperforms all existing methods in terms of precision, recall, and F -measure. The proposed method is implemented in a real-time port and produces more than 98% accuracy in all conditions.https://doi.org/10.1177/1550147718824462 |
| spellingShingle | Jaecheul Lee Deep learning–assisted real-time container corner casting recognition International Journal of Distributed Sensor Networks |
| title | Deep learning–assisted real-time container corner casting recognition |
| title_full | Deep learning–assisted real-time container corner casting recognition |
| title_fullStr | Deep learning–assisted real-time container corner casting recognition |
| title_full_unstemmed | Deep learning–assisted real-time container corner casting recognition |
| title_short | Deep learning–assisted real-time container corner casting recognition |
| title_sort | deep learning assisted real time container corner casting recognition |
| url | https://doi.org/10.1177/1550147718824462 |
| work_keys_str_mv | AT jaecheullee deeplearningassistedrealtimecontainercornercastingrecognition |