Abstract
Research findings on Robotics are discussed in a new report. According to news originating from Bandung, Indonesia, by NewsRx correspondents, research stated, “The ability of the human eye to identify a crescent depends on its apparent object contrast versus the sky background, and inaccurate assessments are common when identifying it. The use of telescopes and cameras to monitor the crescent moon is becoming increasingly important as technology advances.” Funders for this research include Bandung Institute of Technology, Deputy for Strengthening Research and Development, Ministry of Research and Technology, Indonesia. Our news journalists obtained a quote from the research from the Bandung Institute of Technology, “Thus, in this study we developed an automated moon detection system with deep learning and integrated for the robotic telescope OZTALTS with an infrared camera. By utilizing a deep learning method called Mask R-CNN, we have created infrared camera software with the goal of identifying and recognizing the crescent moon. The result shows, a total of 3,202 manually annotated moon images were used for deeplearning- trained models. We tested several combinations of training hyperparameters and image distribution numbers. The results show that the crescent detection issue can be resolved using a Mask R-CNN. Using the top-performing Mask R-CNN configuration, the trained model achieved a mean averaged precision (mAP) at the intersection over union (IOU) of 0.5, with a 99% for the extreme condition of a young crescent concealed by clouds and a 99% for the normal case for each moon phase.”
NETL
NSTL
Elsevier