Supplementation of synthetic object replicas for increasing precision of microrobot trajectory keypoints

Abstract

Artificial neural networks are becoming more popular with the development of artificial intelligence. These networks require large amounts of data to function effectively, especially in the field of computer vision. The quality of an object detector is primarily determined by its architecture, but the quality of the data it uses is also important. In this study, we explore the use of novel data set enhancement technique to improve the performance of the YOLOv5 object detector. Overall, we investigate three methods: first, a novel approach using synthetic object replicas to augment the existing real data set without changing the size of the data set; second - rotation augmentation data set propagating technique and their symbiosis, third, only one required class is supplemented. The solution proposed in this article improves the data set with a help of supplementation and augmentation. Lower the influence of the imbalanced data sets by data supplementation with synthetic yeast cell replicas. We also determine the average supplementation values for the data set to determine how many percent of the data set is most effective for the supplementation.