A Method for Portal Crane Wire Rope Recognition Based on Improved PointNet++

In automated dry bulk terminal operations, accurate perception of the spatial pose of portal crane wire ropes is important for grab positioning and can provide geometric information for subsequent anti-sway control research. Vision-based measurements may be affected by metallic reflections, illumination variation, and dust occlusion, whereas inertial or mechanically coupled measurements may be affected by vibration and dynamic coupling. This study proposes a LiDAR-based method for wire rope point cloud segmentation and pose estimation using an improved PointNet++. Dual-LiDAR point clouds are aligned and filtered using a kinematic constraint-based Region of Interest (ROI) to reduce background redundancy. A Spatial Self-Attention (SSA) module is introduced to combine long-range semantic dependencies with local spatial weighting, improving the representation of sparse and fragmented wire rope points. The segmented wire rope points are separated by t–k-means clustering and fitted with spatial lines for pose estimation. The complete acquisition comprises 11,348 annotated frames: a 9458-frame model development dataset from 1000 complete operating cycles, and a separately retained 1890-frame independent engineering test set from 200 condition-specific operating sequences. The development dataset was divided into mutually exclusive training and validation partitions at the level of complete operating cycles, and checkpoint selection was performed only on the validation set. Three independent training runs with fixed random seeds were conducted. On the independent test set, PointNet++ achieved an F1-score of 87.5 ± 0.2% and an mIoU of 79.0 ± 0.2%, whereas the complete proposed method achieved an F1-score of 92.8 ± 0.2% and an mIoU of 86.6 ± 0.2%. These results characterize performance on independent operating sequences collected from the crane and sensor configurations represented in the dataset. The standalone segmentation stage achieved 111.9 FPS, whereas the complete processing pipeline required slightly more than 2 s per frame because of frame-by-frame KD-ICP fine registration.