Research On Embedded Object Detection Based On Deep Neural Network Compression

Deep neural network is an important research field of artificial intelligence in recent years.Usually,object detection is based on convolutional neural network now.And the basis of convolution operation is matrix computation.Inference progress consumes large amounts of storage and computation resources.But they are limited in embedded devices.Traditional deep learning methods are hard to be used under these circumstances.During inference,parameter values are fixed.On one hand,we don't need the redundancy,on the other hand,the redundancy consumes extra storage and slows the progress.So,it's an important question to eliminate the redundancy of our networks and make it available for embedded object detection environment.To achieve this goal,shrinking parameters' amount and accelerating inference are all needed.The main works are as below: Give a basic theory of convolutional network and its implementation,and some basic technology of convolutional network based object detection.A summary of mainstream neural network's compression and acceleration method now is given.Give some basic idea of their mechanisms and implementations,and compare these method between their pros and cons.Propose a method for CNN-based object detection to compress and accelerate it by sparse channel pruning method from related works.And test this method on an embedded platform Nvidia jetson TX2 with an open source deep learning architecture pytorch.The speed before and after compression and acceleration on Caffe platform are tested.The effectiveness is proved by the project.