Generating real-world-like labelled synthetic datasets for construction site applications

Yair Barrera-Animas, Ari; Davila Delgado, Manuel

Abstract

Having synthetic image generation and automatic labelling as two separate processes remains one of the main limitations of automatic generation of large real-world synthetic datasets. To overcome this drawback, a methodology to perform both tasks in a simultaneous and automatic manner is proposed. To resemble real-world scenarios, a diverse set of rendering configurations of illumination, locations, and sizes are presented. For testing, three synthetic datasets (S, M and SM), oriented to the construction field, were generated. Faster R-CNN, RetinaNet, and YoloV4 detection algorithms were used to independently evaluate the datasets using the COCO evaluation metrics and the PascalVOC Mean Average Accuracy metric. Results show that, in general, the S dataset performed 1.2% better in the evaluation metrics and that the SM dataset obtained better plots of training and validation loss curves in each detector; highlighting the combinational usage of images with single and multiple objects as a better generalisation approach.

Citation

Yair Barrera-Animas, A., & Davila Delgado, M. (2023). Generating real-world-like labelled synthetic datasets for construction site applications. Automation in Construction, 151, Article 104850. https://doi.org/10.1016/j.autcon.2023.104850

• Journal Article Type: Article
• Acceptance Date: Mar 21, 2023
• Online Publication Date: Apr 12, 2023
• Publication Date: Jul 1, 2023
• Deposit Date: Apr 12, 2023
• Publicly Available Date: Apr 18, 2023
• Journal: Automation in Construction
• Print ISSN: 0926-5805
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 151
• Article Number: 104850
• DOI: https://doi.org/10.1016/j.autcon.2023.104850
• Keywords: Object detection, Synthetic dataset, Construction field, Auto-annotation
• Public URL: https://uwe-repository.worktribe.com/output/10622757
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0926580523001103