SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection

The rapidly growing number of space activities is generating numerous space debris, which greatly threatens the safety of space operations. Therefore, space-based space debris surveillance is crucial for the early avoidance of spacecraft emergencies. With the progress in computer vision technology, space debris detection using optical sensors has become a promising solution. However, detecting space debris at far ranges is challenging due to its limited imaging size and unknown movement characteristics. In this paper, we propose a space debris saliency detection algorithm called SDebrisNet. The algorithm utilizes a convolutional neural network (CNN) to take into account both spatial and temporal data from sequential video images, which aim to assist in detecting small and moving space debris. Firstly, taking into account the limited resource of the space-based computational platform, a MobileNet-based space debris feature extraction structure was constructed to make the overall model more lightweight. In particular, an enhanced spatial feature module is introduced to strengthen the spatial details of small objects. Secondly, based on attention mechanisms, a constrained self-attention (CSA) module is applied to learn the spatiotemporal data from the sequential images. Finally, a space debris dataset was constructed for algorithm evaluation. The experimental results demonstrate that the method proposed in this paper is robust for detecting moving space debris with a low signal-to-noise ratio in the video. Compared to the NODAMI method, SDebrisNet shows improvements of 3.5% and 1.7% in terms of detection probability and the false alarm rate, respectively.