Target preposition selection method in image recognition of Android system

Abstract

The invention discloses a target preposition selection method in image recognition of an Android system, and belongs to the technical field of digital image recognition. The method specifically comprises the steps of: enabling a user to select a system service pack on a cloud server according to a to-be-recognized image for downloading, if no corresponding system service pack exists on the cloud server, uploading and then downloading the service pack defined by the user to the cloud server, decompressing the downloaded service pack, decompressing a dex file, a so link library file and a model file, and rebuilding an image recognition function in an Android system to realize preposed selection of a to-be-recognized image, wherein each of the system service pack and the user-defined service pack comprises a model file, a so link library file and a dex file. According to the method, dynamic switching can be achieved for recognition of different types of targets in Android system image recognition, an App packet does not need to be frequently published, and meanwhile the size of the APP packet is not additionally increased.

Description

technical field [0001] The invention belongs to the technical field of digital image recognition, and in particular relates to a method for preselection of targets in image recognition of an Android system. Background technique [0002] With the rapid development of artificial intelligence technology, digital image recognition processing technology has been applied in many fields. At present, the mainstream Android system image recognition application equipment is mainly customized image processing equipment, and customized image processing equipment has real-time intelligence of video stream frames. Processing function. Since Android is a Linux kernel, and the performance of java at the application layer is not as good as that of the underlying C / C++, image recognition on the Android system needs to take into account both real-time and performance requirements, and it is necessary to call the underlying framework through JNI (Java Native Interface). Algorithm, first use th...

AI Technical Summary

Problems solved by technology

[0003] The existing model files are collected and classified in the early stage, pre-processed, converted to format, and batch processed to generate a model file in a specified format. The final trained model file is stored in the resource package assets of the Android program and compiled together with the main program. Generate an app installation package, so it does not have the ability to recognize the diversity and scalability of targets. For example, if the intelligent recognition device realizes the recognition of targets of the type of flame, the system cannot recognize other target objects other than flames, so there are limitations performance, poor scalability

When it is necessary to identify and expand other targets, the existing technology is to develop through incremental programs, that is, retrain to generate model files, and develop new so link library files through NDK, integrate multiple so library files in the APP package and Store multiple image data set model files in the resource directory, so as to complete the recognition function of multiple targets on the APP, which will cause the expansion of the APP package size. The more types of target recognition supported, the larger the package size.

At the same time, the existing technology does not support user-defined requirements. When the user needs to identify the type of target he is interested in, and the system does not have the ability to dynamically generate image data set model files, the image recognition function for the self-defined target cannot be completed.

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