Aiming at the problems of low positioning accuracy and high cost of traditional LiDAR in the process of loading and unloading of automated container terminals, a vision-based three-dimensional measurement system for container attitude is proposed. Firstly, through a small - scale deep learning network for rapid coarse positioning container corner, secondly, the traditional image processing algorithm is used to reposition the container corner pieces to obtain the precise position of the container keyhole, and the threedimensional measurement of the container posture is carried out in combination with the physical movement of the container during the loading and unloading process. The experimental results show that compared with the deep learning network before improvement, the measurement accuracy is higher and the measurement speed is faster, the measurement accuracy of the overall algorithm is 93. 71%, about 12. 45 frames/ s, and the average measurement error of container attitude measurement is about 4. 95%, which meets the requirements of automatic loading and unloading.