Pin-CasNet: Detecting pin status in transmission lines based on cascade network

Fang Gao, Rongwei Zhang, Jingfeng Tang, Shaomin Liu, Wenbo Li, Jun Yu, Changwen Chen, Hanbo Zheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Bolt-pin is an important connection component in transmission lines, and missing pins may disintegrate key components in transmission lines. Because of the extremely small size of the transmission line pins and the complex environment, simply applying a universal object detector cannot well detect the missing pins. This work proposes an automatic method for detecting normal and missing pins in transmission lines based on a cascade network. The two-stage networks can respectively detect local regions that may contain pins and pins in local regions step by step. In addition, this work designs a dual branch selective block (DBSB) attention module for pin detection, which can make the second-stage network extract more robust pin features. Experiments on the transmission line pin datasets show that our method has strong robustness and adaptability in complex environments, and can achieve efficient pin detection.

Original languageEnglish
Article number107244
Pages (from-to)1-12
JournalEngineering Applications of Artificial Intelligence
Volume127
DOIs
Publication statusPublished - Jan 2024

Keywords

  • Cascade network
  • Detection of missing pins
  • Dual branch selective block attention module
  • Transmission lines

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Pin-CasNet: Detecting pin status in transmission lines based on cascade network'. Together they form a unique fingerprint.

Cite this