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.