Rapid identification and localization of dry bulk cargo hoppers are currently core issues in the automation control of gantry cranes at dry bulk terminals. The current conventional method relies on LiDAR systems for the identification and positioning of bulk unloading hoppers. However, this approach is complex and costly. In contrast, GPS-based positioning solutions for bulk unloading hoppers are prone to damage due to the vibrations generated during the operation process. Therefore, in this paper, a hopper localization system based on monocular camera vision is proposed to locate the position of the bulk unloading hopper. The hopper identification and localization process are divided into three stages. The first stage uses the improved YOLOv5 model to quickly and roughly locate the hopper target. The second stage uses morphological geometrical features to locate the corner points of the hopper target. The third stage determines the three-dimensional coordinates of the hopper target by solving the position of the corner points in the world coordinate system through the PnP (Perspective-n-Point) algorithm. The experimental results show that the average positioning accuracy of the coordinates of the method is above 93%, demonstrating the accuracy and effectiveness of the method.

For automated quayside container cranes, accurate measurement of the three-dimensional positioning and attitude of the container spreader is crucial for the safe and efficient transfer of containers. This paper proposes a high-precision measurement method for the spreader’s three-dimensional position and rotational angles based on a single vertically mounted fixed-focus visual camera. Firstly, an image preprocessing method is proposed for complex port environments. The improved YOLOv5 network, enhanced with an attention mechanism, increases the detection accuracy of the spreader’s keypoints and the container lock holes. Combined with image morphological processing methods, the three-dimensional position and rotational angle changes of the spreader are measured. Compared to traditional detection methods, the single-camera-based method for three-dimensional positioning and attitude measurement of the spreader employed in this paper achieves higher detection accuracy for spreader keypoints and lock holes in experiments and improves the operational speed of single operations in actual tests, making it a feasible measurement approach.