Workpiece detection method based on vision

Abstract

The invention discloses a workpiece detection method based on vision. The workpiece detection method is used for calibrating an industrial camera. The method comprises: placing a workpiece in the detection area, the camera is used for photographing the workpiece in the detection area, and an original image of the workpiece in the detection area is obtained; and filtering and denoising the original image; binarizing the image by using an iteration method; and acquiring a plurality of groups of transverse and longitudinal dimension data of the workpiece in a detection area in the image by utilizing an edge detection principle; edge detection can be accurate to unit pixels, so that the detection precision of the method is also a unit pixel point, and the precision is extremely high; if the two edges on the same detection strip exceed the standard size of the workpiece, the workpiece can be judged to be under-processed; on the contrary, if the distance between the two edges is smaller than the standard distance, it can be determined that the workpiece is a defective part; and if the distance between the two edges is equal to the standard distance or within an allowable error range, determining that the workpiece is qualified. The method can help enterprises to reduce the production cost and promote the improvement of the production automation rate.

Description

technical field [0001] The invention relates to a detection method for the combination of machine vision and image recognition for workpiece defects, which is suitable for the occasions of automatic processing and detection of various workpieces, and in particular relates to a vision-based workpiece detection method. Background technique [0002] With the continuous improvement of the automation level of the manufacturing industry, more and more factories have begun to introduce automated production lines, and the automated production of workpieces has gradually replaced the old manual production, and the production efficiency has also been greatly improved. However, in view of whether the size of the workpiece meets the requirements after automatic processing, or whether there is a defect or the processing is not in place, most of them still use a relatively clumsy manual sampling method, which has low efficiency, low accuracy, and large randomness. Problems prevent each pr...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a machine vision-based detection of workpiece defects and inadequate processing for the problems of low production efficiency, poor accuracy, and large randomness caused ...

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