在自动化集装箱码头，自动化智能车辆（automated intelligent vehicle, AIV）代替传统的内部集装箱卡车，在码头的岸区和箱区进行货运。在这个过程中,AIV面临的一个重要挑战是需要主动识别港口环境中的各种障碍物。提出基于深度学习的方法识别港口AIV运行场景中的障碍物，把识别的信息提交到调度系统中，为后续调度规划提供依据。在实验室场景下，使用深度学习可以完成95%的识别正确率。在实际码头应用场景中，该方法达到了90%以上的识别率，并且已实际投入使用。

In automated container terminals， automated intelligent vehicles （AIVs） replace traditional internal container trucks， transporting cargo between the quay and yard areas. A significant challenge faced by AIVs in this process is the proactive identification of various obstacles within the port environment. This paper proposes an obstacle detection approach for port AIV operational scenarios based on deep learning， submitting the identified information to the intelligent electronic container system for subsequent dispatch planning. In laboratory scenarios， the deep learning approach achieves a 95% accuracy rate in recognition. In actual port application scenarios， it attains an identification rate of over 90%， and has been put into practical use.