Design And Implementation Of Adaptive Inference Method System For Image Detection

The research of image detection and adaptive inference has always been a hot research direction in the field of artificial intelligence.It has been widely used in the fields of certificate recognition,unmanned driving and so on.Traditional image detection and adaptive recognition methods usually focus on deep learning model and use multi model fusion or reinforcement learning as auxiliary to achieve the purpose of recognition.This way can effectively improve the effect of the model,but it will also lead to problems such as large amount of calculation,long execution time and difficult to reuse the model effectively.The diversity of Internet devices,the complexity of images required by related services,and the compatibility of related platforms increase the difficulty of image recognition.How to efficiently extract the corresponding characteristics of images and meet the industrial requirements is a strong challenge in the field of image recognition.In view of the above problems,this paper aims to use image preprocessing,convolution network and early leave technology to provide an adaptive system for image recognition with different resolutions and a solution for industrial image API.The main research work of this paper is as follows:Aiming at the problem of image feature value extraction,this paper analyzes the image processing methods in the traditional field of deep learning,and adopts a frequency based feature extraction and learning method.According to the relative frequency of image features,the highfrequency feature image is selected to extract the feature value of image recognition processing.Under the condition of ensuring the accuracy of image recognition,it can effectively improve the calculation and processing efficiency of image recognition and reduce the occupation of computing resources by the algorithm.In view of the inconsistency of image resolution and the decline of eigenvalues after normalization,the full convolution structure and early leave mechanism are used to further adjust the system adaptively.The system can dynamically adjust the execution process of the model for different input samples.In view of the above two parts,combined with the application status of deep learning model in industrial environment,a related system based on Web service server calling API is designed,and a complete set of adaptive algorithm system that can realize authority control is built.