Safe operations of a reach stacker by computer vision in an automated container terminal

Abstract

Smart ports, utilizing advanced and hybrid technologies, are gaining increasing attention for application in the maritime industry, with driver assistance and autonomous driving being pivotal in container-terminal operations. This study introduces a novel approach for enhancing object detection and distance estimation, focusing principally on decision support for reach stacker container handlers in port terminals by integrating generative and deep learning models. The EfficientDet model, enriched with integrated k-means clustering, is developed to detect and classify objects using a practical dataset of labeled images based on visual features. Moreover, generative models, specifically the diffusion model and generative adversarial network, are utilized to generate depth scenes for estimating object distances. Experimental results indicate that the proposed approach yields superior object detection and distance estimation outcomes in port terminal operations, characterized by high accuracy and reduced computational cost. The proposed method exhibits potential for application across various industries, including transportation, logistics, and security, where precise object detection and distance estimation are vital for efficient and secure operations.

Keywords

• Reach stacker
• Automated container terminals
• Computer vision
• Deep learning
• Diffusion model