Generating real-world-like labelled synthetic datasets for construction site applications
Ari Yair Barrera-Animas, Juan Manuel Dávila Delgado
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.