High-speed Parallel Feature Extraction Algorithm of Wind Tunnel Image Based on GPU

Abstract

In wind tunnel test, the serial feature extraction algorithm of wind tunnel image based on CPU is too slow to meet the execution speed requirements of wind tunnel test. To solve this problem, a high-speed parallel feature extraction algorithm of wind tunnel image based on GPU is proposed. The proposed algorithm is optimized in parallel from the CUDA kernel level and CUDA stream level to speed up feature extraction execution. In image pre-processing, a pixel is processed by a CUDA thread to achieve parallelization. In feature extraction, the image segmentation is introduced to parallelize 8-connected boundary tracking algorithm. And a CUDA thread is used to process an image block to parallelize feature extraction process. Furthermore, the proposed algorithm uses CUDA stream to asynchronous data transmission and data processing to achieve CUDA stream level parallelism. To verify the efficiency and effectiveness of the GPU-based algorithm, comparative experiment between CPU and GPU is conducted. The experimental results show that the performance of GPU-based parallel algorithm is far better than that of CPU-based serial algorithm. The GPU-based parallel algorithm can greatly improve the execution speed of feature extraction while ensuring the accuracy of feature data.