Space debris detection with fast grid-based learning

Fast space debris detection is valuable to space safety and space vehicle operation. In this paper, we proposed a fast space debris detection method with grid-based learning. The image divided into 14×14 grids, and then the fast grid-based neural network (FGBNN) is used to locate the position of the space debris in the grids. The experiments show that, In the test set, the model can reach the accuracy of 98.83% and recall of 95.99% with SNR 2 of space debris, it can process 430 images per second with the given experiment settings in the prediction process. It is proved that the proposed method has excellent performance and fast detection speed.