Automated Synthetic Data for Computer Vision: Blender-COCO Pipeline Enhancing Yolov8 - Souza, Victor Roza; Christ, Djonathan Caua Fritzen; Wiese, Igor Scaliante; Naves, Thiago França; Soares, Telma Woerle de Lima; Soares, Anderson da Silva; Berretta, Luciana de Oliverira (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI); 2025: Anais da XXXVIII Conference on Graphics, Patterns and Images, 2025)
This paper introduces an automated and reproducible pipeline for the generation of synthetic datasets with COCO-format annotations, designed to support object detection and segmentation tasks. The proposed framework combines controlled video acquisition of real-world objects, volumetric reconstruction via the SVRaster algorithm, automated mesh postprocessing in Blender, and synthetic rendering with systematically varied lighting and backgrounds. Annotation generation is fully automated through the custom YOLO FOTO plugin, which orchestrates virtual camera placement and metadata export. The resulting synthetic images are employed to train the YOLOv8n model. The study is conducted under the Design Science Research (DSR) methodology, ensuring methodological rigor and reproducibility. Experimental results demonstrate substantial improvements in model performance when synthetic data are incorporated into training, underscoring their effectiveness in enhancing dataset diversity and robustness. These findings highlight the strategic value of synthetic data in scenarios where manual annotation is costly, time-consuming, or otherwise constrained.