发表机构
University of Strathclyde（思克莱德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于DETR的深度学习框架，实现电线杆与标识的检测、分割及倾斜角估算，性能优于多款标准检测器，还公开了自定义数据集作为基准。
AI 中文摘要
电线杆是支撑配电系统及其他关键公共服务的基础设施的重要组成部分，其定期检测对确保电网的稳定性和安全性至关重要。本文提出一种深度学习框架，用于基于地面图像自动检测、分割木质电线杆并估算其倾斜角度，同时对附着的电气警示标识进行分类。该系统在从谷歌街景提取的包含4570张标注图像的自定义数据集上进行训练，这些图像具有视觉模糊、缺乏显著特征的木质电线杆等具有挑战性的真实场景。所提出的模型基于Detection Transformer（DETR），经过适当修改并在自定义数据集上训练，其性能优于标准目标检测器RetinaNet、Faster R-CNN、YOLOv3-Tiny，在电线杆检测上的平均精度均值达90.43%，在标识检测上达88.26%。为该模型添加分割头可实现实例级掩码生成，该掩码随后被用于估算电线杆倾斜角度，模型准确估算了测试集中1433根电线杆中的1367根的倾斜角度，平均绝对误差为1.01度。此外，本研究创建的自定义数据集也被公开提供，可作为基准使用。
英文摘要
Utility poles are an essential part of the infrastructure used to support power distribution systems and other critical public services. Their regular inspection is crucial to ensure the stability and safety of the electrical grid. A deep learning framework is presented for the automated detection, segmentation and lean angle estimation of wooden utility poles, and classification of attached electrical warning signs, using ground-level imagery. The system is trained on a custom dataset of 4,570 annotated images extracted from Google Street View, featuring challenging real-world scenes with visually ambiguous wooden poles lacking distinctive features. The proposed model is based on the Detection Transformer (DETR), suitably modified and trained on the custom dataset. The model outperforms standard object detectors (RetinaNet, Faster R-CNN, YOLOv3-Tiny), achieving a mean average precision of 90.43% for pole detection and 88.26% for sign detection. Extending this model with a segmentation head enables per-instance mask generation, which is then used to estimate pole lean angle. The model accurately estimates lean for 1,367 out of 1,433 test-set poles, with a mean absolute error of 1.01 degrees. Moreover, the custom dataset created in this work is also made publicly available to be used as a benchmark.
Journal refProceedings of the 36th British Machine Vision Conference (BMVC 2025), BMVA, Paper 976, 2025
