发表机构
University of Beira Interior; COFICAB; Instituto de Telecomunicações; NOVA-LINCS（贝拉内瓦大学; 科菲卡布公司; 电信研究所; NOVA LINCS研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CableDex是基于手机单张照片的计算机视觉系统，结合相机标定等技术，经1000张图像训练后，在75个卷轴上实现4.90%的MAPE，可准确估算工业卷轴电缆长度。
AI 中文摘要
CableDex是一种计算机视觉系统，用于解决手动测量工业卷轴上电缆长度耗时且不准确的问题，该系统基于手机拍摄的单张照片即可完成测量。该系统结合了相机标定、实例分割、姿态估计和体积计算技术，用于估算五种不同类型卷轴及各种电缆尺寸下的电缆长度。该系统基于1000张手动标注图像训练的实例分割模型构建，达到99.5%的mAP50，每张图像推理时间为5.66毫秒。在五种类型共75个卷轴上进行评估，该系统的MAPE为4.90%，处于工业电缆卷轴测量普遍接受的10%误差容限范围内。演示展示了端到端流程，从卷轴标签扫描、图像捕获到分割和长度估算，均通过移动应用实现。
英文摘要
CableDex is a computer vision system that addresses the time-consuming and inaccurate manual measurement of cable length on industrial reels from a single photograph captured with a mobile phone. The system combines camera calibration, instance segmentation, pose estimation, and volumetric calculation to estimate the cable length across five different reel types and various cable sizes. This system is based on an instance segmentation model trained on 1,000 manually annotated images, achieving 99.5\% mAP50 with an inference time of 5.66 ms per image. Evaluated on 75 reels across five reel types, the system achieves a MAPE of 4.90\%, within the 10\% error tolerance commonly accepted in industrial cable-reel measurement. The demonstration presents the end-to-end pipeline, from reel label scanning and image capture to segmentation and length estimation, through the mobile application.
