Automated classification of "cluttered" construction housekeeping images through supervised and self-supervised feature representation learning

Construction housekeeping is crucial for safety, but frequent manual inspections are difficult to maintain. A computer vision approach to automatically monitor housekeeping can overcome these issues. However, it re-quires labelling large number of "cluttered" construction housekeeping images that are difficult to label, even by experts. Thus, this paper presents an alternative approach that evaluates the use of self-supervised learning feature extraction to classify "cluttered" construction housekeeping images. The most suitable (84% accuracy) backbone architecture for supervised classification of housekeeping images was found to be Swin-transformer. In addition, the experiments show that self-supervised learning approach can perform better (1-4% improvement in prediction accuracy, precision, and recall) than the supervised learning approach in a non-transfer learning context and when the number of training images is reduced.