For the complex and autonomous landing scene is difficult to be recognized in wild flight environment for low-small-slow Unmanned Aerial Vehicles (UAV), a novel field scene recognition algorithm based on the combination of local pyramid feature and Convolutional Neural Network (CNN) learning feature was proposed. Firstly, the scene was divided into small scenes of 4×4 and 8×8 blocks. The Histogram of Oriented Gradient (HOG) algorithm was used to extract the scene features of all the blocks. All the features were connected end to end to get the feature vector with the characteristics of spatial pyramid. Secondly, a depth CNN aiming at the classification of scenes was designed. The method of tuning training was adopted to obtain CNN model and extract the characteristics of deep network learning. Finally, the two features were connected to get the final scene feature and the Support Vector Machine (SVM) classifier was used for classification. Compared with other traditional manual feature methods, the proposed algorithm can improve the recognition accuracy by more than 4 percentage points in data sets such as Sports-8, Scene-15, Indoor-67 and a self-built one. The experimental results show that the proposed algorithm can effectively improve the recognition accuracy of the landing scene.

With regard to the features of the big skin exposure in the adult images, we propose a hybrid detection approach which is composed of such three parts as color filtering, texture filtering and geometry filtering. A subsection processing skin color model was used to filter the non-skin color pixels and get the candidate skin regions. Then a coarse-degree-based texture filtering was used to filter the pixels with rough texture and skin color tone. At last we filtered the skin-like region (such as deserts or beaches) by fractal dimensions. The experimental results indicate this approach has high detection ratio and high precision.