FPC defect detection method and system based on HSV and CNN, and storage medium

Abstract

The invention discloses an HSV and CNN-based FPC defect detection method and system and a storage medium. The detection method comprises the steps of extracting a detection region in a to-be-detected original image according to color and brightness; converting the to-be-detected original image from an RGB color space to an HSV color space, and preliminarily judging candidate defect areas from the HSV color space according to preset defect parameters; taking an intersection between the candidate defect area and the detection area as a final defect area; comparing the final defect area with a preset threshold value, and judging whether the currently detected FPC is a good product, a defective product or an uncertain product; and identifying the detection area of the uncertain product by using a pre-trained deep learning model, and predicting whether the uncertain product belongs to a good product or a defective product. According to the invention, defect detection is carried out on the FPC to be detected in a mode of combining image processing and a deep learning model, and the method has the advantages of low cost and high precision.

Description

technical field [0001] The present invention relates to the technical field of intelligent manufacturing, and more specifically relates to an FPC defect detection method, system and storage medium based on HSV and CNN. Background technique [0002] The traditional quality inspection for flexible printed circuit boards (FPC for short) mainly relies on manual visual inspection, which is costly and inefficient. With the rapid development of the electronics industry, the design of circuit boards tends to be more and more high-precision and high-density. The traditional manual inspection methods can no longer meet the production needs. The automatic detection of FPC defects has become an inevitable trend of industrial development. [0003] At present, the following methods are commonly used for the automatic detection of FPC defects: (1) The traditional image processing method, according to the color, shape, position and other characteristics of the defect, manually design the co...

AI Technical Summary

Problems solved by technology

This method requires a large number of image samples for long-term training, and also needs to run on the GPU, and the time it takes for the model to infer and predict the target each time is far longer than the traditional image processing method, and it is difficult to adapt to some detections that require high-speed operation. Scenes

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