Segmentation of schlieren images of flow field in combustor of scramjet based on improved fully convolutional network

Extraction of the wave structure of the flow field in the combustor of the scramjet is important for main flow control and performance evaluation of the scramjet. In this study, a deep learning-based method based on the fully convolutional network with 8-pixel stride is proposed to segment the schlieren image to extract the wave structure. First, use a residual neural network with 34 layers as the backbone network to extract features, which ensures highly efficient learning through residual blocks to extract multi-dimension semantic information. Second, dilated convolution is utilized to expand the receptive fields of deepened layers to obtain high-dimensional features and increase the degree of aggregation of contextual information contained in the high-dimensional features. Finally, the channel and spatial attention module are introduced to the decoding stage to enable the model to focus on key information to improve the segmentation accuracy. A large number of experiments are carried out on a dataset of schlieren images of the flow field in the combustor of scramjet that were compiled by the authors. The proposed method recorded higher values of the pixel accuracy, recall, intersection over union, and F1 score than compared methods, with values of 78.47%, 83.81%, 67.51%, and 80.32%, respectively. This method can effectively complete the wave structure extraction and provide important basic support for related research work of scramjet. Published under an exclusive license by AIP Publishing.