Product state detection method and system based on Adaboost algorithm

Abstract

The present invention discloses a product state detection method and system based on an Adaboost algorithm. The method comprises the steps of acquiring interface images of products located in the needed state to generate an interface image sample; carrying out the gray processing on the interface images to obtain a gray images; carrying out the binarization processing on the gray images, and extracting an Haar characteristic; utilizing the Adaboost algorithm to train the Haar characteristic to obtain a classifier template; utilizing the classifier template to classify the interface images of the products acquired on an assembly line, and determining whether the interface images of the products are located in the needed states. According to the technical scheme provided by the present invention, the Adaboost algorithm, the image binaryzation and the Haar characteristic are combined to optimize the product image detection, and an optimization algorithm is introduced in the assembly line to detect the products, thereby being able to detect the products automatically, reducing the labor investment, also being able to satisfy the production takt on the production assembly line.

Description

Technical field [0001] The invention relates to the technical field of automatic production line detection, in particular to a product state detection method and system based on the Adaboost algorithm. Background technique [0002] Smart wearable devices are increasingly entering people's lives, and many smart wearable devices have display screens. The production of smart devices is quite complicated and requires high intelligence. When the production line tests products, it will leave traces of operation in the smart device system. Therefore, it is required to restore the factory settings of the smart device system after the production line function test is completed. Erase the detection traces so that users will not see traces of other people's operations when they get the product, for example, by detecting the boot screen to determine whether the smart device is restored to factory settings. At present, the detection of factory resetting of smart devices by the production lin...

AI Technical Summary

Problems solved by technology

Manual operations will cause misoperations due to operator fatigue and lack of concentration. Although the current camera photo template matching processing method can replace the operator for automatic processing, it is low in efficiency and harmful to the environment due to template matching processing. The requirements are relatively high but the actual efficiency is not high, and the practicability cannot meet the requirements of the production line, which will lead to the flow of products that have not been restored to the factory settings to the hands of users, causing customer complaints and increasing the input of manpower and material resources and increasing costs.

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