Plastic meal box crack detection method and device based on image processing and medium

Abstract

The invention provides a plastic lunch box crack detection method and device based on image processing and a medium. The method comprises the following steps: acquiring a crack image of the surface of a plastic lunch box; performing graying processing and noise reduction processing on the crack image to obtain a processed crack image; carrying out edge detection on the processed crack image by adopting a Canny edge detection method, and determining a crack edge; according to the crack edge, a center line is determined by adopting a center line fitting method based on a least square method, and the crack width is determined according to the center line; and based on a skeleton refinement method, extracting a skeleton graph of the crack according to the crack edge, and determining the length of the crack according to the skeleton graph. According to the technical scheme, the detection efficiency of the cracks of the plastic meal box is improved, and the labor cost is reduced.

Description

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AI Technical Summary

Benefits of technology

This patented technology allows computers to detect damage caused during shipping or handling that may cause cracks in plastic boxes used inside them. It uses specialized equipment like cameras to capture images taken through it while executing programs. By analyzing these captured images, we find patterns indicating small holes called cracks, which indicate how much stress has been placed onto certain parts of the package' s material before they were sent off-shore. These techniques help prevent further damages due to excessive pressure applied over time.

Problems solved by technology

This patented technology describes how faulty or damaged plastic bags cause issues when they're being shipped from manufacturing processes. These flaws could lead to economic loss by reducing their strength and durability overtime. Current methods involve manually checking each bag individually, but these techniques require significant effort and resources compared to automated systems such as machine vision tools.

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