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IoT-Enabled Insulator Crack Detection Using a YOLOv11n Algorithm on Raspberry Pi 5

Students & Supervisors

Student Authors
Fauzia Fariha Trona
Bachelor of Science in Electrical & Electronic Engineering, FE
Farhan Ferdous
Bachelor of Science in Electrical & Electronic Engineering, FE
Ahanaf Tahmid Romit
Bachelor of Science in Electrical & Electronic Engineering, FE
Riad Bin Islam
Bachelor of Science in Electrical & Electronic Engineering, FE
Supervisors
Bishwajit Banik Pathik
Assistant Professor, Faculty, FE

Abstract

Insulators are essential for reliable power transmission. They often develop cracks due to stress, weather, or aging. If these fissures are not found in time, they may spread and cause failures. Traditional inspection techniques are less efficient because they are primarily manual and take a long time. In this research, a lightweight YOLOv11n deep learning model on a Raspberry Pi 5 is used to create an Internet of Things-enabled system for real-time insulator crack detection. This uses a Pi camera Module to take pictures, detects cracks on the gadget, and uses low power wireless communication to send out alarms. According to experimental findings, the method offers precise, quick, and contact free detection, which makes it an effective choice for condition-based tracking and improving grid reliability.

Keywords

IoT insulator crack detection YOLOv11n Raspberry Pi 5 real-time computing.

Publication Details

  • Type of Publication:
  • Conference Name: 11th IEEE WIECON-ECE
  • Date of Conference: 21/12/2025 - 21/12/2025
  • Venue: Long Beach Hotel Cox’s Bazar
  • Organizer: IEEE Bangladesh Section and IEEE WIE
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