Real-Time Anti-Collision Warning Method for Dual Tower-Crane Jibs Based on LiDAR Point-Cloud Segmentation and Swept-Volume Continuous Collision Detection

Dual tower-crane crossing operations require timely identification of collision risks as the spatial relationship between the two jibs changes. Conventional systems based on crane-state sensors and static safety zones provide limited information about the surrounding three-dimensional workspace, while sparse point clouds, occlusion, and class imbalance complicate reliable jib recognition. This study develops a LiDAR-based method that converts point-cloud segmentation into geometric models for short-term collision-risk prediction. A geometric-temporal dynamic graph convolutional neural network (GT-DGCNN) combines geometric priors, class-balanced learning, and temporal consistency to identify the jibs and tower structures of both cranes. The segmented jibs are represented by capsule-based envelopes and evaluated using swept-volume prediction, dynamic safety distance, and time to collision. The complete method achieves a mean intersection over union of 87.3% and a jib recall of 91.7%, exceeding the DGCNN baseline by 6.4 and 7.4 percentage points, respectively. The missed-alarm rate decreases from 7.8% to 2.4%, with a processing rate of 17.2 frames per second. These results support the use of geometric-temporal point-cloud perception for jib recognition and collision warning in the evaluated dual-crane scenarios.