@InProceedings{CerpaSalasMezaLoaiBarb:2020:TrSyIm,
               author = "Cerpa Salas, Alonso Jes{\'u}s and Meza Lov{\'o}n, Graciela 
                         Lecireth and Loaiza Fern{\'a}ndez, Manuel Eduardo and Barbosa 
                         Raposo, Alberto",
          affiliation = "{Universidad Cat{\'o}lica San Pablo} and {Universidad 
                         Cat{\'o}lica San Pablo} and {Universidad Cat{\'o}lica San Pablo} 
                         and {Pontifical Catholic University of Rio de Janeiro}",
                title = "Training with synthetic images for object detection and 
                         segmentation in real machinery images",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Musse, Soraia Raupp and Cesar Junior, Roberto Marcondes and 
                         Pelechano, Nuria and Wang, Zhangyang (Atlas)",
         organization = "Conference on Graphics, Patterns and Images, 33. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "synthetic data generation, object detection, object segmentation, 
                         deep learning.",
             abstract = "Over the last years, Convolutional Neural Networks have been 
                         extensively used for solving problems such as image 
                         classification, object segmentation, and object detection. 
                         However, deep neural networks require a great deal of data 
                         correctly labeled in order to perform properly. Generally, 
                         generation and labeling processes are carried out by recruiting 
                         people to label the data manually. To overcome this problem, many 
                         researchers have studied the use of data generated automatically 
                         by a renderer. To the best of our knowledge, most of this research 
                         was conducted for general-purpose domains but not for specific 
                         ones. This paper presents a methodology to generate synthetic data 
                         and train a deep learning model for the segmentation of pieces of 
                         machinery. For doing so, we built a computer graphics synthetic 3D 
                         scenery with the 3D models of real pieces of machinery for 
                         rendering and capturing virtual photos from this 3D scenery. 
                         Subsequently, we train a Mask R-CNN using the pre-trained weights 
                         of COCO dataset. Finally, we obtained our best averages of 85.7% 
                         mAP for object detection and 84.8% mAP for object segmentation, 
                         over our real test dataset and training only with synthetic images 
                         filtered with Gaussian Blur.",
  conference-location = "Porto de Galinhas (virtual)",
      conference-year = "7-10 Nov. 2020",
                  doi = "10.1109/SIBGRAPI51738.2020.00038",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI51738.2020.00038",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/43BD4BE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/43BD4BE",
           targetfile = "PID6630889.pdf",
        urlaccessdate = "2025, Jan. 15"
}