【摘要】 近年来,形变部件模型和卷积神经网络等卷积检测模型在计算机视觉领域取得了极大的成功。这类模型能够进行大规模的机器学习训练,实现较高的鲁棒性和识别性能。然而训练和评估过程中卷积运算巨大的计算开销,也限制了其在诸多实际场景中进一步的应用。利用数学理论和并行技术对卷积检测模型进行算法和硬件的双重加速。在算法层面,通过将空间域中的卷积运算转换为频率域中的点乘运算来降低计算复杂度;而在硬件层面,利用GPU并行技术可以进一步减少计算时间。在PASCAL VOC数据集上的实验结果表明,相对于多核CPU,该算法能够实现在单个商用GPU上加速卷积过程2.13~4.31倍。更多还原

【Abstract】 In recent years,convolution-based detection models( CDM),such as the deformable part-based models( DPM) and the convolutional neural networks( CNN),have achieved tremendous success in computer vision field. These models allow for large-scale machine learning training to achieve higher robustness and recognition performance. However,the huge computational cost of convolution operation in training and evaluation processes also restricts their further application in many practical scenes. In this paper,we accelerate both the algorithm and hardware of convolution-based detection models with mathematical theory and parallelisation technique. In the aspect of algorithm,we reduce the computation complexity by converting the convolution operation in space domain to the point multiplication operation in frequency domain. While in the aspect of hardware,the use of graphical process unit( GPU) parallelisation technique can reduce the computational time further. Results of experiment on public dataset Pascal VOC demonstrate that compared with multi-core CPU,the proposed algorithm can realise speeding up the convolution process by 2. 13 to 4. 31 times on single commodity GPU.更多还原