In recent years, remote sensing images has become one of the most popular directions in image processing. A small feature gap exists between satellite and natural images. Therefore, deep learning algorithms could be applied to recognize remote sensing images. We propose an improved Mask R-CNN model, called SCMask R-CNN, to enhance the detection effect in the high-resolution remote sensing images which contain the dense targets and complex background. Our model can perform object recognition and segmentation in parallel. This model uses a modified SC-conv based on the ResNet101 backbone network to obtain more discriminative feature information and adds a set of dilated convolutions with a specific size to improve the instance segmentation effect. We construct WFA-1400 based on the DOTA dataset because of the shortage of remote sensing mask datasets. We compare the improved algorithm with other state-of-the-art algorithms. The object detection AP50 and AP increased by 1–2% and 1%, respectively, objectively proving the effectiveness and the feasibility of the improved model.

In the process of flight for in-orbit spacecraft, debris and particles in space will collide with the spacecraft at a high speed. These collision will produce strain, pit or perforation on the surface of the spacecraft cabin, forming damage accumulation. Therefore, it is necessary to monitor the strain and impact position of the spacecraft cabin in real time, which will provide a basis for ensuring the reliability and safety of the spacecraft. In this paper, the achievement of strain and vibration information during the process of impact is designed in two ways: touched style and untouched style. The design of touched style needs to meet the requirements of large capacity/ultra-high speed monitoring. The design of untouched style needs to meet the acquisition of high frequency vibration signal in the impact process.

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