TY  - EJOU
AU  - Zhang, Ping 
AU  - Li, Chungang 
AU  - Song, Jian 
AU  - Tan, Jianjun 
AU  - Tian, Han 
AU  - Chen, Le 
AU  - Song, Yongkun 

TI  - A Point Cloud Segmentation Method for Adhering Coal Piles in Industrial Coal Sheds
T2  - Journal on Artificial Intelligence

PY  - 2026
VL  - 8
IS  - 1
SN  - 2579-003X

AB  - Accurate segmentation of coal pile point clouds is essential for automated inventory management and safety supervision in industrial coal sheds. With the widespread adoption of LiDAR technology, massive point cloud data are available, yet traditional clustering algorithms often fail to distinguish physically adhering coal piles in high-density storage scenarios, resulting in severe under-segmentation. To address this challenge, this paper proposes an automated point cloud segmentation framework tailored for complex industrial environments. The proposed framework consists of three core stages: data preprocessing, ground segmentation, and instance segmentation. First, relative elevation mapping and selective statistical filtering are employed to normalize coordinate baselines and suppress noise. Then, a Normal-Constrained Random Sample Consensus (NC-RANSAC) algorithm is introduced to separate horizontal ground surfaces from pile slopes using geometric angular constraints. Finally, an unsupervised strategy based on Principal Component Analysis (PCA) and sliding-window concavity detection is developed to identify saddle points and decouple adhering instances. Experimental results on real-world industrial datasets demonstrate that the NC-RANSAC algorithm exhibits high parameter stability and effectively resists interference from non-ground planes. Furthermore, the proposed instance segmentation strategy successfully decouples adhering coal piles with varying morphologies without manual intervention or pre-trained models.
KW  - Point cloud segmentation; industrial coal sheds; adhering coal piles

DO  - 10.32604/jai.2026.080463