Abstract：The dock is an important hub connecting water transportation and land transportation. However, due to the complex working environment of the loading machine on the dock, operators may face many problems during the loading process, such as the inability to fully monitor the operation process, and the emission of ash/dust during loading. In order to alleviate this situation, this paper proposes an assisted ship loading model based on deep learning algorithms. Based on advanced YOLOv7 and YOLOv8 models, it can monitor the loading process 24 hours a day, predict the position and height of the ship loading spout in real time, calculate the material deviation and detect ash emission during loading. In addition, when the ship loading spout approaches the walls of the cargo hold, a warning will be issued to avoid collision.
汪 阳, 王润生, 李占宇. 基于深度学习的水泥发运码头辅助装船模型研发及应用[J]. 水泥杂志, 2024, 0(05): 30-.
WANG Yang, et al.. Research and application of auxiliary loading model of cement shipping terminal based on deep learning. Journal of Cement, 2024, 0(05): 30-.
2024, Vol. 0 
