A Crane Wire Rope Lifting Ratio Detection Method Based on SegFormer

This paper addresses the critical challenge of automated lifting ratio detection for crane wire ropes, a key parameter for operational safety traditionally reliant on manual observation or sensor-based methods. We propose a novel SegFormer-based segmentation model enhanced with a decoder-integrated self-attention module, which significantly improves global contextual reasoning and spatial precision in complex industrial environments. Extensive evaluation on a dedicated multi-ratio dataset demonstrates that our method achieves 93.31% mIoU, 96.37% mPA, and 98.84% aAcc, outperforming strong baselines including SegFormer, U-Net, YOLOv11-seg, and RT-DETR. The model further exhibits notable robustness to noise, illumination changes, and occlusion, validating its practical applicability for real-world crane monitoring systems.