False Ceiling Deterioration Detection and Mapping Using a Deep Learning Framework and the Teleoperated Reconfigurable ‘Falcon’ Robot

Sensors

Rajesh Elara Mohan¹  Lee Ming Jun Melvin¹  Lim Yi¹  Archana Semwal¹  Povendhan Palanisamy¹  Balakrishnan Ramalingam¹  Sathian Pookkuttath¹  Chanthini Baskar² 

[1] Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore;School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, India;

关键词: defect detection;    Faster R-CNN;    deep learning;    object detection;    IoRT;    inspection robot;   

DOI  :  10.3390/s22010262

来源: DOAJ

【 摘 要 】

Periodic inspection of false ceilings is mandatory to ensure building and human safety. Generally, false ceiling inspection includes identifying structural defects, degradation in Heating, Ventilation, and Air Conditioning (HVAC) systems, electrical wire damage, and pest infestation. Human-assisted false ceiling inspection is a laborious and risky task. This work presents a false ceiling deterioration detection and mapping framework using a deep-neural-network-based object detection algorithm and the teleoperated ‘Falcon’ robot. The object detection algorithm was trained with our custom false ceiling deterioration image dataset composed of four classes: structural defects (spalling, cracks, pitted surfaces, and water damage), degradation in HVAC systems (corrosion, molding, and pipe damage), electrical damage (frayed wires), and infestation (termites and rodents). The efficiency of the trained CNN algorithm and deterioration mapping was evaluated through various experiments and real-time field trials. The experimental results indicate that the deterioration detection and mapping results were accurate in a real false-ceiling environment and achieved an 89.53% detection accuracy.

【 授权许可】

Unknown