国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

The drastic increase of the number of space debris in orbit around the earth poses a serious threat to operational spacecraft. Rapid small space debris detection with space-based surveillance platform is very important for spacecraft emergency avoidance at distance. Nevertheless, small space debris detection is a great challenge due to its fast movement and noise caused by cosmic rays in space-based surveillance platform. Inspired by strong pattern recognition capacity of deep learning, a deep convolutional neural network based small space debris saliency detection method for space-based surveillance system is proposed. First, the spatial contrast map of the input image is obtained using local contrast method. Then, the spatiotemporal saliency information is captured incorporating with the above contrast map. Last, the experiments are conducted based on the synthetic video sequence images. It can detect the space debris with the furthest distance 30 km and the smallest size of 16 pixels. The experimental results prove the applicability and robustness of the proposed method through different Gaussian white noise variance set up simulating the cosmic noise.