No abstract

Monitoring the vital signs of a developing embryo is very useful in avian breeding programs, especially during early days of incubation, so that dead or unfertilized eggs can be timely removed from incubator and new eggs can be placed in. A noninvasive system for detecting the vital signs of avian embryo through intact egg in early stage of incubation has been developed using laser speckle imaging (LSI). The system was based on the measurement of intensity fluctuations of speckle caused by the embryo’s blood flow in the intact egg under laser light illumination. This system was found to be feasible in imaging the vasculature in the egg as well as confirming its fertilization or survival from the second day to fifth day of incubation while other reported noninvasive methods cannot detect vital signs of the embryo until the sixth day of incubation.

3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval. We introduce the Objectron dataset to advance the state of the art in 3D object detection and foster new research and applications, such as 3D object tracking, view synthesis, and improved 3D shape representation. The dataset contains object-centric short videos with pose annotations for nine categories and includes 4 million annotated images in 14, 819 annotated videos. We also propose a new evaluation metric, 3D Intersection over Union, for 3D object detection. We demonstrate the usefulness of our dataset in 3D object detection tasks by providing baseline models trained on this dataset. Our dataset and evaluation source code are available online at http://www.objectron.dev.