基于无监督语义分割的架空线路缺陷识别
Overhead Line Defect Recognition Based on Unsupervised Semantic Segmentation
AI总结：
本文提出一种结合 Faster RCNN 与无监督语义分割的架空线路缺陷识别框架，通过设备定位、背景分割及相似性与逻辑规则判定缺陷，提升了识别准确率与适应性。
摘要：
架空线路巡检极大受益于利用可见光图像进行的缺陷识别。针对现有特征提取技术的局限性以及深度学习方法对数据的严重依赖，本文提出了一种新颖的缺陷识别框架。该框架构建于 Faster RCNN 网络之上，并辅以无监督语义分割。该方法包括识别目标设备的类型和位置，利用语义分割区分设备及其背景，最后采用相似性度量和逻辑规则对缺陷类型进行分类。实验结果表明，该方法在识别架空线路问题时更关注设备而非缺陷本身。这带来了准确率的显著提升，并展现出令人瞩目的适应性。因此，该研究为配电网设备巡检自动化提供了一个全新的视角。
英文摘要：
Overhead line inspection greatly benefits from defect recognition using visible light imagery. Addressing the limitations of existing feature extraction techniques and the heavy data dependency of deep learning approaches, this paper introduces a novel defect recognition framework. This is built on the Faster RCNN network and complemented by unsupervised semantic segmentation. The approach involves identifying the type and location of the target equipment, utilizing semantic segmentation to differentiate between the device and its backdrop, and finally employing similarity measures and logical rules to categorize the type of defect. Experimental results indicate that this methodology focuses more on the equipment rather than the defects when identifying issues in overhead lines. This leads to a notable enhancement in accuracy and exhibits impressive adaptability. Thus, offering a fresh perspective for automating the inspection of distribution network equipment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
