【摘要】 为解决在嵌入式设备上实时、高精度检测司机安全驾驶监督的问题,本文基于目标检测中经典的深度学习神经网络YOLOv3-tiny,运用通道剪枝技术成功在目标检测任务中实现了模型压缩,在精度不变的情况下减少了改进后神经网络的计算总量和参数总数.并基于NVIDIA的推理框架TensorRT进行了模型层级融合和半精度加速,部署加速后的模型.实验结果表明,加速模型的推理速度约为原模型的2倍,参数体积缩小一半,精度无损失,实现了高精度下实时检测的目的.更多还原

【Abstract】 In order to optimize the method of real-time and high-precision detection of drivers’ safe driving supervision,based on the classic deep learning neural network-YOLOv3-tiny-in object detection, this study successfully uses the channel pruning technology to achieve model compression in the object detection task, and reduces the calculated total amount and parameters of the improved neural network under the condition of constant accuracy. Based on NVIDIA’s inference platform TensorRT, model level fusion and half-precision acceleration are performed, and the accelerated model is deployed. The experimental results show that the speed of inference of the acceleration model is about 2 times that of the original model, the parameter volume is reduced by half, and the accuracy is not lost, which realizes the purpose of real-time detection under high precision.更多还原