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GPU acceleration design method for driver’s seatbelt detection

机译：用于驾驶员安全带检测的GPU加速设计方法

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摘要

With the development and maturity of deep learning algorithms, CNN have emerged in the field of computer vision. Image recognition is one of the important research directions in the field of computer vision. The traditional image recognition method is to extract features by constructing feature descriptors and then classify them by classifiers, such as gradient direction histogram and support vector machine. These methods generally have the problems of poor robustness and insufficient ability to extract features in complex application scenarios. At the same time, convolutional neural network has not been well applied in image recognition due to its large amount of computation and slow speed. With the development of GPU, the parallel computing capability has been greatly improved. This paper designs a GPU acceleration method for the driver’s seatbelt detection system based on CNN. The system is based on the Deconv-SSD target detection algorithm for vehicle detection, the Squeeze-YOLO algorithm for vehicle front windshield location, and the semantic segmentation for seat belt detection. Based on the characteristics of GPU, through the off-line merging bath normlization and convolution layer, Tensorrt model conversion technology to realize the GPU optimization speed. The results show that the proposed acceleration method can effectively improve the detection efficiency.
机译：随着深度学习算法的发展和成熟，CNN已经出现在计算机视觉领域。图像识别是计算机视觉领域的重要研究方向之一。传统的图像识别方法是通过构造特征描述符来提取特征，然后通过梯度方向直方图和支持向量机等分类器对特征进行分类。这些方法通常存在鲁棒性差和在复杂应用场景中提取特征的能力不足的问题。同时，由于卷积神经网络计算量大，速度慢，因此在图像识别中还没有得到很好的应用。随着GPU的发展，并行计算能力得到了极大的提高。本文设计了基于CNN的驾驶员安全带检测系统的GPU加速方法。该系统基于用于车辆检测的Deconv-SSD目标检测算法，用于车辆前挡风玻璃定位的Squeeze-YOLO算法以及用于安全带检测的语义分割。根据GPU的特性，通过离线合并浴规范化和卷积层，Tensorrt模型转换技术来实现GPU优化速度。结果表明，所提出的加速方法可以有效地提高检测效率。

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