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Wang, J., He, M., Zhang, Y., Zhang, Z., Postolache, O. & Mi, C. (2025). High-precision pose measurement of containers on the transfer platform of the dual-trolley quayside container crane based on machine vision. Sensors. 25 (9)

J. Wang et al.,  "High-precision pose measurement of containers on the transfer platform of the dual-trolley quayside container crane based on machine vision", in Sensors, vol. 25, no. 9, 2025

@article{wang2025_1777286535764,
	author = "Wang, J. and He, M. and Zhang, Y. and Zhang, Z. and Postolache, O. and Mi, C.",
	title = "High-precision pose measurement of containers on the transfer platform of the dual-trolley quayside container crane based on machine vision",
	journal = "Sensors",
	year = "2025",
	volume = "25",
	number = "9",
	doi = "10.3390/s25092760",
	url = "https://www.mdpi.com/journal/sensors"
}

TY  - JOUR
TI  - High-precision pose measurement of containers on the transfer platform of the dual-trolley quayside container crane based on machine vision
T2  - Sensors
VL  - 25
IS  - 9
AU  - Wang, J.
AU  - He, M.
AU  - Zhang, Y.
AU  - Zhang, Z.
AU  - Postolache, O.
AU  - Mi, C.
PY  - 2025
SN  - 1424-8220
DO  - 10.3390/s25092760
UR  - https://www.mdpi.com/journal/sensors
AB  - To address the high-precision measurement requirements for container pose on dual-trolley quayside crane-transfer platforms, this paper proposes a machine vision-based measurement method that resolves the challenges of multi-scale lockhole detection and precision demands caused by complex illumination and perspective deformation in port operational environments. A hardware system comprising fixed cameras and edge computing modules is established, integrated with an adaptive image-enhancement preprocessing algorithm to enhance feature robustness under complex illumination conditions. A multi-scale adaptive frequency object-detection framework is developed based on YOLO11, achieving improved detection accuracy for multi-scale lockhole keypoints in perspective-distortion scenarios (mAP@0.5 reaches 95.1%, 4.7% higher than baseline models) through dynamic balancing of high–low-frequency features and adaptive convolution kernel adjustments. An enhanced EPnP optimization algorithm incorporating lockhole coplanar constraints is proposed, establishing a 2D–3D coordinate transformation model that reduces pose-estimation errors to millimeter level (planar MAE-P = 0.024 m) and sub-angular level (MAE-0 = 0.11°). Experimental results demonstrate that the proposed method outperforms existing solutions in container pose-deviation-detection accuracy, efficiency, and stability, proving to be a feasible measurement approach.
ER  -