Improving Insulators Detection Accuracy via Image Enhancement Techniques: Case of Indigenous Aerial Image Dataset

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access Pub Date : 2024-10-04 DOI:10.1109/ACCESS.2024.3474255

Shafi Muhammad Jiskani;Tanweer Hussain;Anwar Ali Sahito;Faheemullah Shaikh;Laveet Kumar

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引用次数: 0

Abstract

The challenging task of insulator monitoring through aerial images is addressed in high voltage transmission network and highlights the limitations of traditional human patrolling with emphasize on utilization of unmanned aerial vehicles UAVs utilizing machine learning algorithms. This research has been accomplished by creating indigenous dataset of 500kV transmission network of National Transmission and Despatch Center Limited (NTDCL). 608 original images were captured in diverse lighting and topographical conditions which were then augmented to 3618 images. To improve the detection accuracy of YOLOv8s algorithm in aerial images, HSV and CLAHE image enhancement techniques were employed to improve the visual feature of insulator with suppressed noise. YOLOv8s algorithm with image enhancement has improved detection accuracy from 88% to 95% demonstrating the effectiveness of integrating image enhancement technique for insulator monitoring, offering promising improvement in maintenance practices and operational reliability of transmission lines.

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通过图像增强技术提高绝缘子检测精度：本土航空图像数据集案例

通过航空图像对高压输电网络中的绝缘子进行监测是一项极具挑战性的任务，它凸显了传统人工巡视的局限性，并强调了利用机器学习算法使用无人机 UAV 的重要性。这项研究通过创建国家输电调度中心有限公司（NTDCL）500 千伏输电网络的本地数据集来完成。在不同的光照和地形条件下拍摄了 608 幅原始图像，然后将其添加到 3618 幅图像中。为提高 YOLOv8s 算法在航空图像中的检测精度，采用了 HSV 和 CLAHE 图像增强技术，以改善绝缘体的视觉特征，抑制噪声。采用图像增强技术的 YOLOv8s 算法将检测准确率从 88% 提高到 95%，证明了将图像增强技术用于绝缘子监测的有效性，有望改善输电线路的维护实践和运行可靠性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

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