Intelligent Crater Detection on Planetary Surface Using Convolutional Neural Network

Crater extraction and recognition is an important research content of deep space planetary science. Traditional crater detection algorithms (CDAs) are mainly based on crater feature construction which relies on high-quality data. With the application of deep learning in image recognition and semantic segmentation, new ideas have been brought to meteorite crater extraction. Many crater extraction algorithms based on artificial intelligence have been proposed which greatly simplifies the crater extraction process and improves detection accuracy. However, with the improvement of the accuracy, the convolution kernels become more and more, and the huge parameters and the consumption of storage and computing resources limit the application of these algorithms in mobile device. In order to solve this problem, we propose a compact crater extraction network based on model pruning. In the combination of U-Net and residual block, the network structure is optimized under the premise of retaining the longitude of large model extraction, and the balance of hardware resources and algorithm accuracy is achieved. The experimental results show that we compress the model to 4.9% of raw size and only lose almost 0.5% accuracy. It provides a reference for the application of CDAs on resource constrained platforms.