Work

Insulator Defect Detection Algorithm Based on Improved YOLO11s in Snowy Weather Environment

Year: 2025

Type: article

Abstract: The intelligent transformation of power systems necessitates robust insulator condition detection to ensure grid safety. Existing methods, primarily reliant on manual inspection or conventional image processing, suffer significantly degraded target identification and detection efficiency under extreme weather conditions such as heavy snowfall. To address this challenge, this paper proposes an enhanced YOLO11s detection framework integrated with image restoration technology, specifically targeting insulator defect identification in snowy environments. First, data augmentation and a FocalNet-based snow removal algorithm effectively enhance image resolution under snow conditions, enabling the construction of a high-quality training dataset. Next, the model architecture incorporates a dynamic snake convolution module to strengthen the perception of tubular structural features, while the MPDIoU loss function optimizes bounding box localization accuracy and recall. Comparative experiments demonstrate that the optimized framework significantly improves overall detection performance under complex weather compared to the baseline model. Furthermore, it exhibits clear advantages over current mainstream detection models. This approach provides a novel technical solution for monitoring power equipment conditions in extreme weather, offering significant practical value for ensuring reliable grid operation.

Source: Symmetry

DOI: 10.3390/sym17101763

Authors: Ziwei Ding, Song Deng, Qingsheng Liu

Institutions: Nanjing University of Posts and Telecommunications, State Grid Corporation of China (China)

Language: English

Topic: Power Line Inspection Robots

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $2,896