Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore.
Sensors (Basel). 2022 Jul 12;22(14):5201. doi: 10.3390/s22145201.
Cleaning is an important task that is practiced in every domain and has prime importance. The significance of cleaning has led to several newfangled technologies in the domestic and professional cleaning domain. However, strategies for auditing the cleanliness delivered by the various cleaning methods remain manual and often ignored. This work presents a novel domestic dirt image dataset for cleaning auditing application including AI-based dirt analysis and robot-assisted cleaning inspection. One of the significant challenges in an AI-based robot-aided cleaning auditing is the absence of a comprehensive dataset for dirt analysis. We bridge this gap by identifying nine classes of commonly occurring domestic dirt and a labeled dataset consisting of 3000 microscope dirt images curated from a semi-indoor environment. The dirt dataset gathered using the adhesive dirt lifting method can enhance the current dirt sensing and dirt composition estimation for cleaning auditing. The dataset's quality is analyzed by AI-based dirt analysis and a robot-aided cleaning auditing task using six standard classification models. The models trained with the dirt dataset were capable of yielding a classification accuracy above 90% in the offline dirt analysis experiment and 82% in real-time test results.
清洁是一个在各个领域都要进行的重要任务，具有首要重要性。清洁的重要性催生了许多新的家庭和专业清洁技术。然而，对于各种清洁方法所提供的清洁程度的审核策略仍然是手动的，并且经常被忽视。这项工作提出了一个新的家庭污垢图像数据集，用于清洁审核应用，包括基于人工智能的污垢分析和机器人辅助清洁检查。基于人工智能的机器人辅助清洁审核的一个重大挑战是缺乏全面的污垢分析数据集。我们通过识别九种常见的家庭污垢类别和一个包含 3000 张显微镜污垢图像的标记数据集来弥补这一差距，这些图像是从半室内环境中采集的。使用粘性污垢提取方法收集的污垢数据集可以增强当前的污垢感应和清洁审核中的污垢成分估计。使用基于人工智能的污垢分析和机器人辅助清洁审核任务对数据集的质量进行了分析，使用了六个标准分类模型。使用污垢数据集训练的模型在离线污垢分析实验中能够达到 90%以上的分类精度，在实时测试结果中能够达到 82%。
