For satellite interactions missions such as autonomous docking, the key to successful completion of these missions is to autonomously and accurately detect targets’ information. The missions with high precision requirement need not only to obtain the categories and location of target satellite components but also to obtain the high-level information such as the corner point. In this paper, a satellite components detection (SCD) method based on improved CenterMask is proposed, which is the state-of-the-art instance segmentation network, to autonomously and accurately detect the target components. First, the fully convolutional one-stage object detection detector is optimized to better obtain categories and bounding boxes of targets. Next, a spatial-channel attention module is proposed, and it is introduced in the mask branch to improve the performance of segmentation. Finally, a satellite components dataset is built for model training, and the optimized SCD model is obtained after training. To better satisfy missions with a higher speed requirement, a speed-up method is also proposed, which greatly improves the detection speed at the expense of very little accuracy. Experiments show that, compared with CenterMask, our method improves 2.8 and 1.5% of box average precision ($AP$) and mask $AP$, and the speed-up version improves the speed of 5.2 frames per second.

对于诸如自动对接之类的卫星交互任务，成功完成这些任务的关键是自主且准确地检测目标的信息。具有高精度要求的任务不仅需要获取目标卫星组件的类别和位置，而且还需要获取诸如拐点之类的高级信息。本文提出了一种基于改进的CenterMask的卫星分量检测（SCD）方法，该技术是最新的实例分割网络，用于自主准确地检测目标分量。首先，对全卷积单级目标检测检测器进行优化，以更好地获得目标的类别和边界框。接下来，提出一个空间通道注意模块，并将其引入mask分支以提高分割性能。最后，建立了卫星分量数据集进行模型训练，并在训练后获得了优化的SCD模型。为了更好地满足对更高速度要求的任务，还提出了一种提速方法，以非常少的精度为代价大大提高了检测速度。实验表明，与CenterMask相比，我们的方法将盒子平均精度提高了2.8和1.5％（$\mathrm{一个}P$）和遮罩 $\mathrm{一个}P$，并且加速版本将每秒的速度提高了5.2帧。