Pig Detection Using Computer Vision Models Trained on Synthetic Datasets - Martins, Alvaro V.; Paixão, Thiago M.; Boldt, Francisco A.; Condotta, Isabella C. (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional, 2025)
Pork is a major commodity in the global food industry, yet monitoring pigs in their living spaces remains a challenge due to the scarcity of annotated datasets for AI models. In this study, an existing method for generating training datasets by utilizing a limited number of annotated images is adapted for pig detection, with the goal of enabling accurate pig counting in future applications. Using the COCO dataset and the Multi-Camera Pig Dataset, we applied Digital Image Processing and Meta’s Segment Anything Model 2 (SAM2) to augment data. A YOLO model trained on the generated datasets demonstrated that manual annotation can be reduced by 40% with minimal performance loss, significantly decreasing dataset production effort.