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.
Sensors (Basel). 2021 Dec 30;22(1):262. doi: 10.3390/s22010262.
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.
定期检查吊顶是确保建筑物和人员安全的强制性要求。一般来说，吊顶检查包括识别结构缺陷、供暖、通风和空调（HVAC）系统退化、电线损坏和虫害。人工协助的吊顶检查是一项费力且危险的任务。本工作提出了一种使用基于深度神经网络的目标检测算法和遥控“猎鹰”机器人进行吊顶劣化检测和测绘的框架。该目标检测算法使用我们的自定义吊顶劣化图像数据集进行训练，该数据集由四个类别组成：结构缺陷（剥落、裂缝、麻面和水渍）、HVAC 系统退化（腐蚀、模具和管道损坏）、电气损坏（磨损电线）和虫害（白蚁和啮齿动物）。通过各种实验和实时现场试验评估了训练好的 CNN 算法和劣化测绘的效率。实验结果表明，在真实的吊顶环境中，劣化检测和测绘结果准确，检测准确率达到 89.53%。
