TY - JOUR
T1 - Smart Pipe Inspection Robot with In-Chassis Motor Actuation Design and Integrated AI-Powered Defect Detection System
AU - Zholtayev, Darkhan
AU - Dauletiya, Daniyar
AU - Tileukulova, Aisulu
AU - Akimbay, Dias
AU - Nursultan, Manat
AU - Bushanov, Yersaiyn
AU - Kuzdeuov, Askat
AU - Yeshmukhametov, Azamat
N1 - Publisher Copyright:
Authors
PY - 2024
Y1 - 2024
N2 - In the contemporary world, inspection operations have become a critical component of infrastructure maintenance. Over the years, the demand for comprehensive inspection of pipes, both internally and externally, has grown increasingly complex and challenging. Consequently, there is a pressing need for significant advancements in in-pipe robots, particularly in the areas of inspection speed, defect detection precision, and overall reliability. Recent developments in new devices and sensors have markedly improved our capability to inspect and diagnose defects within pipes with greater accuracy. Furthermore, the application of machine learning tools has optimized the inspection process, enhancing the detection and recognition of potential pipe defects, such as rust, blockages, and welding anomalies. This research introduces a novel mobile robot platform specifically designed for pipe inspection. It integrates an advanced machine learning model that effectively detects and identifies key pipe defects, including rust, compromised welding quality, and pipe deformation. Additionally, this platform offers enhancements in inspection speed. The integration of these technologies represents a significant stride in the field of infrastructure maintenance, setting a new standard for efficiency and precision in pipe inspection.
AB - In the contemporary world, inspection operations have become a critical component of infrastructure maintenance. Over the years, the demand for comprehensive inspection of pipes, both internally and externally, has grown increasingly complex and challenging. Consequently, there is a pressing need for significant advancements in in-pipe robots, particularly in the areas of inspection speed, defect detection precision, and overall reliability. Recent developments in new devices and sensors have markedly improved our capability to inspect and diagnose defects within pipes with greater accuracy. Furthermore, the application of machine learning tools has optimized the inspection process, enhancing the detection and recognition of potential pipe defects, such as rust, blockages, and welding anomalies. This research introduces a novel mobile robot platform specifically designed for pipe inspection. It integrates an advanced machine learning model that effectively detects and identifies key pipe defects, including rust, compromised welding quality, and pipe deformation. Additionally, this platform offers enhancements in inspection speed. The integration of these technologies represents a significant stride in the field of infrastructure maintenance, setting a new standard for efficiency and precision in pipe inspection.
KW - Computational modeling
KW - Defect detection
KW - Image edge detection
KW - Inpipe robot
KW - Inspection
KW - Machine learning
KW - Machine learning
KW - Pipe inspection
KW - Robot design
KW - Robots
KW - Sensors
KW - Simultaneous localization and mapping
KW - SLAM
UR - http://www.scopus.com/inward/record.url?scp=85202718995&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85202718995&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3450502
DO - 10.1109/ACCESS.2024.3450502
M3 - Article
AN - SCOPUS:85202718995
SN - 2169-3536
SP - 1
JO - IEEE Access
JF - IEEE Access
ER -