Based on deep learning AI algorithms, it can accurately detect defects in glass bottles

Various glass packaging applications are widespread, such as wine, pharmaceutical packaging, and daily chemical packaging. In the production process of glass bottles, due to the complexity of its manufacturing process, defects are difficult to avoid, posing serious hidden dangers to product quality. Common flaws include issues with the bottle body (such as bubbles, irregularities, cracks, and stains), bottle mouth inspection (such as gaps, burrs, and skewed bottlenecks), and bottle bottom (such as cracks and incorrect bottom dimensions).

The traditional manual inspection mode has obvious limitations. Not only is the inspection efficiency low and difficult to meet the needs of large-scale production, but it is also greatly affected by the characteristics of glass bottles, such as easy reflection and subtle defect types, which significantly increase the difficulty of inspection. This often leads to missed and false inspections, making it impossible to guarantee inspection accuracy.

For work scenarios like glass bottle inspection, which require high levels of repetition and precision, as well as efficiency, Keyi Technology adopts an AI visual inspection solution based on deep learning AI algorithms. This solution breaks through the difficulties of traditional inspection technology, collects product defect samples, and can learn and train autonomously. It can identify subtle differences between samples from multiple dimensions, simulate massive data with a small number of samples, and adapt to different products and defects, providing stable detection results in complex environments. The self-developed industrial cameras and light sources restore the true color of the product, determining the accuracy of inspection from the source, leaving no room for flawed defects.