Abstract
The Synthetic Aperture Radar (SAR) target detection using Adaptive Boosting (AdaBoost) based on Haar-like feature is accelerated via Graphics Processing Unit (GPU) in this paper. As a machine learning algorithm, AdaBoost has achieved great success in the field of target detection. However, due to the time-consuming training process, it is difficult to achieve real time requirements, which limits its further development. In this paper, based on the analysis of the algorithm, the AdaBoost algorithm based on Haar-like feature is parallel decomposed and then implemented by using Moving and Stationary Target Recognition (MSTAR) dataset to improve the detection efficiency. First, the AdaBoost algorithm based on Haar-like feature is investigated. Then, in order to improve the detection speed, the algorithm is parallel decomposed. Finally, the algorithm is implemented by Compute Unified Device Architecture (CUDA) for acceleration to see the acceleration effect. According to the experiments, the time spent on the training process and the testing process has been greatly reduced by using CUDA. Compared to the traditional CPU-based AdaBoost algorithm based on Haar-like feature, the algorithm using CUDA parallel computing can achieve a speedup of 30.
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