Instance segmentation of objects in images using deep learning and synthetic data

The paper considers the problem of instance segmentation of objects in images using modern deep learning models and synthetic data. The main attention is paid to the study of the effectiveness of synthetic data created on the basis of 3D models for pre-training segmentation models. Such architectures as U-Net, DeepLabV3+, Mask R-CNN and YOLOv8 are considered. To improve the quality of synthetic data, various parameters of automatic data generation were used, including random positioning of objects, adding backgrounds, changing lighting, changing object texture, adding blur and adding obstacles. The experiments showed that each of these steps significantly contributes to the accuracy of the models, and their combination provides the best results (mAP 92.1%). The results confirm that the combined use of synthetic and real data allows bridging the gap between the synthetic and real environment. The best performance was achieved by the YOLOv8 model, which demonstrated high accuracy and processing speed. The obtained findings highlight the importance of carefully tuning the parameters of synthetic data generation to improve segmentation in real-world applications.