Product appearance detection method based on cloud edge collaborative model optimization and implementation system thereof

Abstract

The invention relates to a product appearance detection method based on cloud edge collaborative model optimization and an implementation system thereof. The detection method comprises the following steps: S1, establishing a basic data set; s2, training a YOLOv3-tiny model and a YOLOv3 model, and respectively deploying the YOLOv3-tiny model and the YOLOv3 model at an edge server and a cloud platform; s3, detecting and recognizing, by the YOLOv3-tiny model, the picture, when the picture is detected to be qualified, sending the picture and a detection resultto the cloud platform, and executing the S5, if the detection result is unqualified, determining that the result is suspected to be unqualified, sending the result to the cloud platform, and performing S4; s4, performing secondary detection on the suspected unqualified pictures on the cloud platform; if the secondary detection is not qualified, outputting a result, and ending the process, and if the secondary detection is qualified, performing S5; and S5, storing the qualified picture in the cloud platform, outputting a result, and ending. According to the method, the problems of accuracy, flexibility, time delay and data utilization rate are solved by using a working mode of cloud edge collaboration.

Description

technical field

[0001] The invention relates to a product appearance detection method based on cloud-edge collaborative model optimization and its realization system, belonging to the technical field of edge computing architecture and artificial intelligence. Background technique

[0002] The detection rate of industrial production lines is directly related to production efficiency. At present, most production lines use a fixed-model air-conditioning external unit identification method. The identification success rate is not high, and it is not easy to update and deploy the model. There are problems in the collection and processing of a large amount of industrial data. .

[0003] The working mode of most existing factory production lines does not involve the cloud edge architecture, and there is still room for improvement in detection accuracy, problem analysis, and overall optimization. However, edge computing has high requirements for scene personalization, and its ration...

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