外观
论文arXiv 2610.1762015 天前
基于智能手机的现场部署式配电基础设施 AI 巡检系统
原文链接：Smartphone-Based, Field-Deployed AI Inspection System for Power Distribution Infrastructure（arXiv 2610.17620 · 发布日期：2026-10-13）
Smartphone-Based, Field-Deployed AI Inspection System for Power Distribution Infrastructure
提出基于智能手机的AI配电巡检流程，结合图像验证、深度学习、单目深度和规则处理，在APEPDCL五条馈线现场试点验证。
核心要点
- 人工巡检地理分散的配电网络难以一致、规模化执行，亟需现场可用的辅助检查方案。
- 系统从现场手机图像出发，结合质量与视角验证，评估杆倾、横担和顶夹倾斜等状态。
- 在APEPDCL五条选定馈线试点，识别1,222个缺陷实例，84个记录为已整改。
- 公开PD-Defect现场采集多模块数据集；中断记录不足以证明可靠性提升归因于系统。
验证范围与局限：验证范围为APEPDCL五条选定配电馈线的现场试点，可靠性提升未由现有观测充分证明。
英文摘要 (Abstract)
Routine inspection of power distribution infrastructure remains challenging for utilities operating geographically dispersed networks, where conventional manual inspection can be difficult to perform consistently and at scale. This work presents a smartphone-based AI inspection system for assessing targeted distribution-infrastructure conditions from images captured by field personnel. The proposed workflow integrates image-quality and viewing-perspective validation with AI-assisted assessment of pole lean, crossarm and top-cleat tilt, voltage level, vegetation-clearance risk, and distribution-transformer (DTR)-ground clearance. The system integrates deep-learning models with monocular depth estimation, geometric analysis, and rule-based processing within a common multi-stage inspection workflow designed for practical field operation. The system was pilot deployed across five selected distribution feeders within the Eastern Power Distribution Company of Andhra Pradesh Ltd. (APEPDCL), where 1,222 defect instances were identified across the assessed categories and subsequently tracked through a rectification and verification workflow, with 84 defect instances recorded as rectified during the study period. Feeder-level interruption records were also examined over the study period; however, the available observations do not provide sufficient evidence to establish a reliability improvement attributable to the proposed system. A curated multi-module field-captured dataset, PD-Defect, is also publicly released to support reproducible research in distribution-infrastructure inspection. The field deployment demonstrates the practical integration of smartphone-based AI-assisted inspection into field inspection and maintenance activities, providing a mechanism for systematic identification, documentation, rectification tracking, and verification of targeted distribution-infrastructure defects.
作者：Sampath Balaji Masabathula, Srija Vanapalli, Gangadhar Kanchi, Sreenivasa Rao V. A. N, Vijaya Lalitha V, Sasank Chilamkurthy
来源
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