Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide-field Small Aperture Telescopes

Abstract

Wide field small aperture telescopes are widely used for optical transient observations. Detection and classification of astronomical targets in observed images are the most important and basic step. In this paper, we propose an astronomical targets detection and classification framework based on deep neural networks. Our framework adopts the concept of the Faster R-CNN and uses a modified Resnet-50 as backbone network and a Feature Pyramid Network to extract features from images of different astronomical targets. To increase the generalization ability of our framework, we use both simulated and real observation images to train the neural network. After training, the neural network could detect and classify astronomical targets automatically. We test the performance of our framework with simulated data and find that our framework has almost the same detection ability as that of the traditional method for bright and isolated sources and our framework has 2 times better detection ability for dim targets, albeit all celestial objects detected by the traditional method can be classified correctly. We also use our framework to process real observation data and find that our framework can improve 25% detection ability than that of the traditional method when the threshold of our framework is 0.6. Rapid discovery of transient targets is quite important and we further propose to install our framework in embedded devices such as the Nvidia Jetson Xavier to achieve real-time astronomical targets detection and classification abilities.

The Deep Neural Network based Photometry and Astrometry Framework for Wide Field Small Aperture Telescopes

“…Comparing with our faster-rcnn based framework in Jia et al (2020b), we have added the astrometry and photometry neural network branches at the end of the box regression. The box regression neural network would output types and rough positions of astronomical targets (bounding boxes with four boundary pixels to indicate their position and classification results to indicate types of different celestial objects).…”

“…According to our experience, the bounding box regression in original faster-rcnn based astronomical target detection framework could return position accuracy better than 1 pixel (Jia et al 2020b), which is enough to cross-match stars in catalogue for WFSATs. The astrometry neural network in the PNET could obtain positions of stars with higher accuracy (better than 0.01 pixel for stars with moderate brightness), therefore we will leave along the performance of the astrometry neural network and test the performance of the photometry neural network, which is our main target for development of the PNET.…”

“…Besides there are no structural noises in simulated images (cosmic rays, hot pixels or clouds), which will lead to unrealistic good results for the SExtractor: accuracy is almost 1 for bright targets. Detailed comparisons of detection abilities between the PNET and the SExtractor with real observed images can be found in Jia et al (2020b). Besides, for dim targets, the PNET is better than the SExtractor in target detection tasks.…”

“…For targets with small size, two step detection frameworks have better performance (Ren et al 2015). Therefore we have proposed to use feature pyramid nets and resnet (He et al 2016) to form backbone neural networks and use faster-rcnn structure to form a two step astronomical target detection framework to process images obtained by WFSATs (Jia et al 2020b). Tested with simulated and real observation data, our framework has shown robust performance in detection of astronomical targets of different types.…”

Space Debris Detection Using Feature Learning of Candidate Regions in Optical Image Sequences

“…Kong et al [25] proposed an effect analysis of the optical masking (EAOM) algorithm, which can detect GEO space debris with a low SNR based on a top-hat transformation, masking technique, and weighted algorithm. Jia et al [42] built an astronomical object detection and classification pipeline based on the Faster R-CNN, ResNet-50 [43] and Feature Pyramid Network [44]. These frameworks have a strong ability to detect dim small targets with high accuracy.…”