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Oct 14, 2024

Case Studies: Glass Vial Inspection: Detect errors on reflective surfaces

Member Since 2018 LEARN MORE senswork is an expert in machine vision systems and specializes in optical inspection, industrial image processing and testing equipment manufacturing. Our ready-to-use

Member Since 2018

LEARN MORE

senswork is an expert in machine vision systems and specializes in optical inspection, industrial image processing and testing equipment manufacturing. Our ready-to-use camera technologies for automation and quality assurance are used every day by our renowned customers in numerous industries.

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POSTED 08/16/2024

Glass vials are essential in various industries, particularly pharmaceuticals, where they are used to store and transport delicate substances. Ensuring the integrity and cleanliness of these vials is critical, as even minor defects like fractures or dirt can compromise the product's safety and effectiveness. However, the transparent and reflective nature of glass presents significant challenges for traditional inspection methods.

The primary challenge in inspecting glass vials lies in differentiating between genuine defects (such as fractures) and benign irregularities (such as dirt or reflections). Traditional rule-based machine vision systems often struggle to make these distinctions, especially when defects or dirt are located on different layers of the glass (front or back). The high variability in lighting conditions and the inherent transparency of glass exacerbate these difficulties.

ViDi Detect, an AI-based deep learning tool, was implemented to address these challenges. Unlike traditional machine vision systems, ViDi Detect can be trained to recognize and distinguish subtle variations in the glass surface, enabling it to accurately identify defects even under challenging conditions.

The process began by creating a reference set of "good" vials, capturing images of these under various lighting conditions and with different amounts of natural dirt. This reference set was then used to train the ViDi Detect system, teaching it to differentiate between acceptable variations and true anomalies.

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After training, ViDi Detect demonstrated a remarkable ability to accurately detect fractures and dirt on glass vials and ensured that only genuinely defective vials were flagged for further inspection.

The implementation of ViDi Detect for the inspection of glass vials has proven to be a game-changer. By leveraging deep learning, it overcomes the limitations of traditional machine vision, providing a reliable and efficient solution for ensuring the quality and safety of glass vials in critical applications.