To the prominent issues of insufficient recognition accuracy,frequent manual intervention,and excessive weighing detection costs in the container gate system during the automated transformation of terminals,this paper conducts a systematic study based on machine vision and deep learning technologies.The research enhances the reliability of vehicle number matching by constructing a dual-binding mechanism for license plate recognition,replaces traditional weighbridge devices with video intelligent recognition technology,and develops a multi-dimensional container damage detection algorithm.As a result,the automatic release rate of the gate,the accuracy of container number recognition,and the accuracy of damaged area positioning have all exceed 98%,and the accuracy of damaged type recognition has reached 92%.Meanwhile,the feature extraction algorithm is used to accurately detect the weighing status of the front and rear wheels of container trucks,increasing the efficiency of weighing detection operations by 230% compared to manual methods.The full-process appointment of gate operations is realized through a mobile app,combined with an intelligent path guidance function for automatic navigation,the problem of limited wharf site reconstruction can be effectively solved.This research provides a replicable technical paradigm for the automated upgrading of traditional terminals and has important practical value for comprehensively improving the intelligence level of ports.