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About A Thesis On Automated Handling of Port Containers Using Machine Learning
This thesis examines automating the handling of port containers using machine learning. It aims to reduce human labor, save time, and lower costs compared to manual container handling. The authors propose using object recognition with supervised machine learning. Input and output data will be labeled for a machine learning algorithm to detect containers. Google TensorFlow will be used for its object detection API and Python interface. Several Python packages like NumPy, Matplotlib and OpenCV will support attaining the goal. The trained model achieved over 98% accuracy detecting customized container prototypes. An automated container handling prototype was then developed that could autonomously handle containers.
Detailed Information
| Author: | ['Ashifur Rahaman'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 59 |
| Language: | English |
| Format: | |
| Price: | FREE |
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