Abstract:
Due to the presence of background noise in natural scenes and the interference of complex factors such as illumination, rotation, and shooting angle, it is very difficult to identify the image of buildings in natural scenes. Aiming at the dependence of traditional building extraction methods on human design and the improvement of building edge feature extraction algorithm.Through the Keras framework to obtain the bottleneck layer of convolutional neural networks (CNN) model MobileNet,and add a new classifier for transfer learning. A large number of data augmentation and test set augmentation are applied to the input image. After three versions of transfer learning, high accuracy was achieved within 480 iterations in three test set. Compared with other feature extraction algorithms, CNN has the advantages of non-transformation and automatic extraction of features, achieves higher accuracy in a shorter period of time. At the same time, MobileNet weight only occupy 15.3 MB with high precision and less calculation, which can be widely transplanted to mobile devices. The system based on model migration has the functions of photo recognition, photo album recognition, menu display, etc., providing mobile platform users with a convenient and simple tool to quickly and accurately obtain the information of buildings in natural scenes.
