Feature Learning and Analysis for Cleanliness Classification in Restrooms
By Lahiru Jayasinghe, Nipun Wijerathne, Chau Yuen. 2019
11 citations recorded by the source corpus.
In order to revamp the cleaning contract from the head-count basis into a performance basis, a fair and unbiased cleanliness classification is necessary. However, the perception of cleanliness is very subjective to the observer. Hence, it is not an easy task to quantify the cleanliness. This paper presents an application of principal component analysis (PCA) in conjunction with convolutional neural networks (CNN) to identify the cleanliness of a restroom up to three levels; namely, dirty, average, and clean. The proposed method includes an application specific data augmentation algorithm and a PCA-based feature analysis schema to select the best suited CNN model for our dataset. Since this study focused on a specific application, we benchmark the performances of the proposed method performances with the state-of-the-art computer vision algorithms on our dataset. Moreover, our study shows a machine learning approach toward automating the inspection process of a restroom.
Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering
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In Computer Vision and Pattern Recognition
Citations in the corpus
- A survey of data fusion in smart city applications (2019, background): “• L1: Sensor Level Fusion At the sensor scale, data from various physical sensors is fused to form an output such as [53], [96].”