GPU acceleration design method for driver’s seatbelt detection

Abstract

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.