Mengjie He
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Mengjie He has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Chao Mi
College of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, China
5 shared publications - Yang Shen
Higher Technical College, Shanghai Maritime University, Shanghai 201306, China
4 shared publications - Yujie Zhang
School of Technology and Architecture, ISCTE-Instituto Universitário de Lisboa, 1649-026 Lisbon, Portugal
4 shared publications - Octavian Postolache
Instituto de Telecomunicações, ISCTE-Instituto Universitário de Lisboa, 1649-026 Lisbon, Portugal
3 shared publications - Xintai Man
Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
3 shared publications - Jiaqi Wang
Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
1 shared publication
Latest Publications
- Open Access
- 14 March 2026
- Published by Springer Nature
- p. 473-482
Application Research of Digital Twin System in Operation Risk Assessment of Container Gantry Crane Drivers
- 14 March 2026
- Published by Springer Nature
- p. 473-482
Open AccessApplication Research of Digital Twin System in Operation Risk Assessment of Container Gantry Crane Drivers
- Open Access
- 14 March 2026
- Published by Springer Nature
- p. 464-472
Design of a Virtual LiDAR System Simulation Training Platform for Port Automation Equipment Based on Digital Twin
- 14 March 2026
- Published by Springer Nature
- p. 464-472
Open AccessDesign of a Virtual LiDAR System Simulation Training Platform for Port Automation Equipment Based on Digital Twin
- Open Access
High-Precision Pose Measurement of Containers on the Transfer Platform of the Dual-Trolley Quayside Container Crane Based on Machine Vision
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- = 0.11°). 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.Open AccessHigh-Precision Pose Measurement of Containers on the Transfer Platform of the Dual-Trolley Quayside Container Crane Based on Machine Vision
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- = 0.11°). 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. - Open Access
- 5 November 2024
- Published by MDPI AG in Algorithms
- Vol. 17 (11) , 508
Automated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality
Currently, the operational risk assessment of quay crane operators at ports relies on manual evaluations based on experience, but this method lacks objectivity and fairness. As port throughput continues to grow, the port accident rate has also increased, making it crucial to scientifically evaluate the risk behaviors of operators and improve their safety awareness. This paper proposes an automated evaluation method based on a Deep Q-Network (DQN) to assess the risk behaviors of quay crane operators in virtual scenarios. A risk simulation module has been added to the existing automated quay crane remote operation simulation system to simulate potential risks during operations. Based on the collected data, a DQN-based benchmark model reflecting the operational behaviors and decision-making processes of skilled operators has been developed. This model enables a quantitative evaluation of operators’ behaviors, ensuring the objectivity and accuracy of the assessment process. The experimental results show that, compared with traditional manual scoring methods, the proposed method is more stable and objective, effectively reducing subjective biases and providing a reliable alternative to conventional manual evaluations. Additionally, this method enhances operators’ safety awareness and their ability to handle risks, helping them identify and avoid risks during actual operations, thereby ensuring both operational safety and efficiency.
- 5 November 2024
- Published by MDPI AG in Algorithms
- Vol. 17 (11) , 508
Open AccessAutomated Evaluation Method for Risk Behaviors of Quay Crane Operators at Ports Using Virtual Reality
Currently, the operational risk assessment of quay crane operators at ports relies on manual evaluations based on experience, but this method lacks objectivity and fairness. As port throughput continues to grow, the port accident rate has also increased, making it crucial to scientifically evaluate the risk behaviors of operators and improve their safety awareness. This paper proposes an automated evaluation method based on a Deep Q-Network (DQN) to assess the risk behaviors of quay crane operators in virtual scenarios. A risk simulation module has been added to the existing automated quay crane remote operation simulation system to simulate potential risks during operations. Based on the collected data, a DQN-based benchmark model reflecting the operational behaviors and decision-making processes of skilled operators has been developed. This model enables a quantitative evaluation of operators’ behaviors, ensuring the objectivity and accuracy of the assessment process. The experimental results show that, compared with traditional manual scoring methods, the proposed method is more stable and objective, effectively reducing subjective biases and providing a reliable alternative to conventional manual evaluations. Additionally, this method enhances operators’ safety awareness and their ability to handle risks, helping them identify and avoid risks during actual operations, thereby ensuring both operational safety and efficiency.