Vision-Based Measurement and Detection in Container Operations
Summary
Vision-based measurement and detection systems have become central to the automation and safety of container handling at ports and terminals. By leveraging advances in image processing, deep learning and three-dimensional reconstruction, these systems enable non-contact, real-time assessment of container orientation, positioning and attachment integrity. Key applications include precise attitude measurement of spreaders, detection of corner castings and lock holes, automated truck-lifting prevention and draft reading for ships in quayside operations. Together, these technologies improve operational throughput, reduce reliance on manual inspection and enhance safety by identifying misalignments or potential accidents before they occur. Global container throughput growth and the drive towards fully automated terminals have fuelled research into robust algorithms that can withstand varied lighting conditions, occlusions and environmental interferences. Integrating multispectral imaging, attention-based neural networks and traditional morphological processing has yielded detection accuracies on the order of millimetres, meeting the stringent demands of modern logistics chains.
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Vision-Based Measurement and Detection in Container Operations publication trend
The graph below shows the total number of articles in vision-based measurement and detection in container operations across all publications each year (not limited to Nature Index journals).
Technical terms
Three-dimensional attitude: The orientation of an object in three-dimensional space, typically described by roll, pitch and yaw angles.
Spreader: A lifting appliance attached to a crane for gripping and handling shipping containers.
Keypoint detection: The process of identifying specific coordinates on an object, such as corners or holes, used for precise localisation.
Convolutional neural network (CNN): A class of deep learning models designed to process grid-like data, notably images, through convolutional layers for feature extraction.
Attention mechanism: A neural network component that enables the model to focus on relevant parts of input data, improving detection accuracy.
References
- Design and Implementation of 3-D Measurement Method for Container Handling Target. Journal of Marine Science and Engineering (2022).
- Improved YOLOv5 Network for High-Precision Three-Dimensional Positioning and Attitude Measurement of Container Spreaders in Automated Quayside Cranes. Sensors (2024).
- Truck‐Lifting Prevention System Based on Vision Tracking for Container‐Lifting Operation. Journal of Advanced Transportation (2021).
- Smart Ship Draft Reading by Dual-Flow Deep Learning Architecture and Multispectral Information. Sensors (2024).
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