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2025 Vol.35, Issue 6 Preview Page

Original Article

Development of Drone and AI Based Framework for Exterior Inspection and Damage Classification of Tower Cranes

드론 및 AI 기반 타워크레인 외관 점검 및 손상 분류 체계화

Hojun Yang1
,
Seungdo Kim2*
,
Yosoon Choi3*
양 호준1
,
김 성도2*
,
최 요순3*
1Master's Student, Dept. of Energy Resources & Geosystems Engineering, Sejong University
2Vice President, Geolux Co., Ltd.
3Professor, Dept. of Energy Resources & Geosystems Engineering, Sejong University
1세종대학교 에너지자원공학과 석사과정
2(주)지오룩스 부사장
3세종대학교 에너지자원공학과 교수
*Corresponding Author
31 December 2025. pp. 741-759
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Abstract

This study presents a systematic framework for defining inspection items and classifying damage types to facilitate the automation of tower crane visual inspections using drone-based imaging and artificial intelligence (AI)-based image analysis. Ten major structural components of tower cranes (foundation, mast, bracing, access platform, telescopic cage, turntable, operator’s cabin, counter jib, main jib, and hook block) were analyzed by integrating legal inspection standards, technical guidelines, and accident case studies to derive detailed inspection items. In addition, seven major categories of visual damage—cracks and fractures, corrosion and surface deterioration, deformation, fastening defects, wire rope and sheave damage, hydraulic and electrical failures, and safety device defects—were systematically classified. Quantitative criteria for each category were established to standardize AI training datasets and support the development of automated damage detection models. The proposed framework provides a technical foundation for constructing smart inspection platforms and advancing automation technologies for the safety assessment of construction equipment.

Keywords
Tower crane
Drone
Artificial intelligence
Visual inspection
Damage classification

본 연구에서는 타워크레인의 외관검사 자동화를 위해 드론 영상과 인공지능(Artificial Intelligence, AI) 영상 분석 기술을 결합한 점검 항목 정의 체계 및 손상 유형 분류 체계를 정립하였다. 타워크레인의 10개 주요 구성요소(기초부, 마스트, 브레싱, 안전플랫폼, 텔레스코픽 케이지, 턴테이블, 운전실, 카운터붐, 메인붐, 훅블록)를 대상으로 관련 법정 검사 기준, 기술 지침, 사고 사례를 종합적으로 분석하여 구성요소별 세부 점검 항목을 도출하였다. 또한 외관 손상 유형을 7개 대분류(균열 및 파손, 부식 및 표면 열화, 변형, 체결 불량, 와이어로프 및 시브 손상, 유압·전기 손상, 안전장치 결함)로 체계화하고, 각 유형별 정량 기준을 제시함으로써 AI 학습용 데이터셋 구축과 자동 탐지 모델 개발에 활용 가능한 표준화된 정의 체계를 마련하였다. 본 연구의 결과는 향후 스마트 점검 플랫폼 구축과 건설기계 안전진단 자동화 기술의 고도화를 위한 핵심 기초 자료로 활용될 수 있을 것으로 기대된다.

키워드
타워크레인
드론
인공지능
외관검사
손상 유형
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Information
  • Publisher :Korean Society for Rock Mechanics and Rock Engineering
  • Publisher(Ko) :한국암반공학회
  • Journal Title :Tunnel and Underground Space
  • Journal Title(Ko) :터널과 지하공간
  • Volume : 35
  • No :6
  • Pages :741-759
  • Received Date : 2025-10-25
  • Revised Date : 2025-11-04
  • Accepted Date : 2025-11-06
  • DOI :https://doi.org/10.7474/TUS.2025.35.6.741
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