AI Adds Intelligent Defect Detection to Machine Vision Toolbox

With population growth comes increasing consumer demands for everything from food to electronics to textiles and more. To meet these demands, industry is increasingly using more automation, especially where human labor is in short supply. Automation is also better suited for performing repetitive tasks, such as assembly, filling and packaging, defect detection, and materials handling. 

Industrial automation technologies, including machine vision and robotics, have played a central role in reducing material waste and improving product quality, manufacturing productivity, product traceability, and adherence to standards and regulations. Pairing these well-established technologies with AI-enabled software brings their capabilities and value for businesses to the next level. 

Protect Against Labor Woes

In addition to being costly, labor — both skilled and unskilled — is becoming increasingly difficult to source. The U.S. Chamber of Commerce reported that as of June 2024, there were approximately 6.8 million unemployed people in the United States against a pool of 8.1 million open positions. In other words, if every person in the country seeking a job had one, there would still be more than a million vacant positions. Throughout the pandemic, human labor was in even shorter supply, and although the gap has narrowed, manufacturers and other businesses often struggle to fill open positions. The solution for many companies has been to automate.

While capital costs for adopting and deploying automation may seem high, the long-term benefits have been proving themselves for years. This is particularly true in environments where human operators would be at increased physical risk, because of repetitive tasks, heavy lifting, awkward posturing, or exposure to hazardous materials. Now, with instability in the labor force and awareness of the impacts of a sudden labor shortage, automation is providing a new element of value by mitigating the potential disruption to manufacturing operations that a lack of human labor can create.

Cut Costs, Maintain Brand Reputation 

Within industrial automation, machine vision has emerged as one of its fastest-growing segments. Vision systems, once confined to verification and quality inspection, are now present in a nearly infinite list of application types, ranging from robots and robotic arm guidance to item and part identification, palletizing, gauging and measurement, and defect detection. 

Ensuring Quality Through Early Defect Detection

Modern and robust methods of reliably and accurately identifying defects as soon as possible — long before a product makes its way into the hands of a consumer — are critical in today’s economy, where consumers have high expectations for quality products. Defects can range from cosmetic issues (scratches, blemishes, and discoloration) to structural issues (cracks and missing screws) to functional issues (improper electrical connections and missing components). 

Beyond the Visible: Machine Vision’s Role in Advanced Quality Control

Looking beyond physical defects and omissions, machine vision inspection systems can also identify nonvisible quality issues. For example, in food and beverage applications, machine vision can be used to verify the placement of seals on food packaging. It can also be used to detect spoilage in agricultural and food products before it is visible to the eye and to determine the presence of allergens and other contaminants before products make their way into the hands of consumers. Product recalls are costly, in terms of dollars, time, and the potential loss of brand reputation. Quality control using modern machine vision mitigates these risks and the associated liability to manufacturers while simultaneously increasing confidence that the health and safety of consumers and end-users are being maintained.

AI Expands Machine Vision Toolbox

Overcoming the Limitations of Traditional Quality Inspection with AI

Traditional quality inspection has relied on computer vision — based on rigid algorithms designed to find a specific defect or flaw in a product. The challenge with these methods is that if the item being inspected is inconsistent or its orientation changes, the algorithms are prone to failure. Machine learning and deep learning offer solutions to this challenge with “intelligent” algorithms — those capable of delivering accuracy despite inconsistencies in the inspection environment. In addition, the ability to train and update algorithms results in continuous improvement in the accuracy of the inspection process, driving efficiency by improving yields and lowering manufacturing costs. Oxipital AI gives manufacturers in food and beverage and beyond a novel, easy-to-integrate set of AI tools designed to achieve exactly these benefits.

Streamlining Inspection with Pretrained Object Models

The system uses a proprietary synthetic data-generation pipeline that develops pretrained object models, so users are not burdened with tasks like providing or labeling images. The pretrained object models are designed with deep object understanding to provide users with the highest degree of performance and flexibility, enabling them to maintain control of the operation through a robust set of no-code tools. This allows for the creation of human-readable rules, allowing for rapid testing, iteration, and highly reliable deployments.

A Complete AI Vision Solution: Oxipital AI’s 3D RGB Cameras and User-Friendly Software

The Oxipital AI solution incorporates purpose-designed 3D RGB cameras packaged in food-grade IP69K enclosures, which facilitate easy cleaning and sanitization in food processing or other applications with a risk of contamination or water and fluid exposure. A sophisticated yet easy-to-use software interface eliminates the complexity of deploying AI for machine vision. With pretrained object models and a set of no-code application builder tools, the system is ready to go out of the box.

Adaptable, Accurate Systems Through AI

Machine vision has an increasingly important role to play in quality inspection and manufacturing applications. As products, packaging, and regulatory requirements become more complex, conventional computer vision can prove limited. In addition, fluctuating labor pools can create inconsistency in manufacturing throughput and in the quality of each item produced. 

AI-enabled software expands machine vision abilities and opens the door to deploying automation in applications where human inspectors have traditionally been necessary. This toolbox of capability results in systems that are more adaptable and flexible while enhancing the accuracy and quality of the finished product.