Research On Edge Calculation Scheme Of Object Detection Model

As a technology in machine learning algorithms,deep learning has greatly promoted the development of machine learning and has attracted the attention of Internet companies and related researchers from all over the world.However,deep learning models often have high performance requirements for the computing platform while providing excellent accuracy,which increases the storage and computing pressure of the platform.Therefore,we are studying how to reduce the weight of large-size models,improve model operation speed,and reduce memory consumption.Improving the performance of the algorithm and reducing the throughput and calculation of the model has become a research hotspot in recent years.Because the edge computing platform has the characteristics of portability,versatility,and high energy efficiency,it is becoming the key to deep learning deployment.Research on the efficient deep learning deployment method at the edge has strong practical significance.Based on this background,this thesis is based on the current direction of computer vision target detection in deep learning,researches the current optimal design method and the structure of the deep target detection model,and studies the structural design of lightweight model and lightweight transformation method of the deep target detection model.In response to the current research pain points of this research direction,this thesis designs and implements a single-stage deep target detection API for edge computing based on Tensor Flow 2.0.Based on this API,the research on the lightweight scheme of neural network for edge computing is carried out.The current cutting-edge lightweight target detection network is redesigned,and the accuracy of the network is successfully improved while reducing the amount of calculation.The network is optimized and quantified,and a comparative analysis of accuracy is given.At the same time,we explore the deployment of deep learning models on different hardware for edge computing,and deploys deep detection models on various hardware including high-performance embedded GPU platforms,Android platforms,ARM platforms,and heterogeneous chip platforms.Under the condition that the platform framework isn't natively supported,the implementation method of the customized model operator for the platform is explored,and a comparative analysis is given.Finally,four complete hardware deployment schemes are given for the three platforms.