Real-Time Wire Rope Inclination Detection Using YOLOv9-Based Camera–LiDAR Fusion for Overhead Cranes

Safe and efficient cargo handling is essential in modern port logistics, where overhead cranes are widely used to move containers, bulk materials, and heavy equipment. Accurate real-time measurement of wire rope inclination is critical for preventing collisions, reducing load sway, and enabling autonomous crane operation under challenging maritime conditions. This paper presents a You Only Look Once v9 (YOLOv9)-based camera–LiDAR fusion system for real-time estimation of the trolley–hook rope inclination angle in overhead cranes. A monocular industrial camera and a YOLOv9 detector provide semantic region-of-interest (ROI) masks for the trolley and hook, while a 3D LiDAR sensor, rigidly mounted and extrinsically calibrated to the camera, provides depth information. LiDAR points projected onto the image and filtered by YOLOv9 bounding boxes allow efficient extraction of safety-critical 3D geometry and reconstruction of the rope vector. Experimental results on an overhead crane testbed show that the proposed fusion estimator achieves an angle RMSE below 1 degree in dynamic swing and low-illumination scenarios, significantly outperforming a camera-only baseline (RMSE ≈ 2.11). These metrically validated results indicate that the proposed detection pipeline offers a robust foundation for intelligent crane monitoring and automation in maritime logistics and smart port operations.