All You Need To Know About Google’s Visual Inspection AI
In 2019, Google Cloud identified six sectors as vital components of its growth: public, healthcare, financial services, retail, media, and manufacturing. Within manufacturing, the cost of quality control and inspection continues to be among the highest. The American Society for Quality estimates that the price of quality may be as high as 15 to 20 percent of annual sales revenues for many organisations. For larger manufacturers, this translates into billions of dollars every year. Additionally, the rapid increase in production volumes makes it difficult for humans to manually inspect defects in computer chips and other products. To combat this, Google Cloud has recently announced an approach, backed by artificial intelligence (AI), for visual inspection. The newly launched Visual Inspection AI is a purpose-built tool to help manufacturers and related workers and businesses to inspect and reduce product defects and decrease quality control costs. Powered by Google Cloud Platform’s computer vision technology, Visual Inspection AI goes beyond the traditional methods of supporting manufacturing quality control through its general-purpose AI product, AutoML. According to Kevin Prouty, Group Vice President of Energy and Manufacturing at IDC, “Google Cloud’s approach to visual inspection is the roadmap most manufacturing companies are looking for.” Visual Inspection AI aims to automate quality assurance workflows, thus allowing companies to identify and correct defects before shipping products. Through this, the new AI tool automates visual inspection using a set of AI and computer vision to improve production by increasing yields, reducing re-work, and cutting back on return-and-repair costs. Previous methods COVID-19 has increasingly driven manufacturers to adopt AI into their production processes. According to a Google Cloud survey, 76 percent of executives say they have embraced digital enablers such as AI, data analytics and cloud computing. Additionally, 66 percent of manufacturers who use AI in their daily operations have stated that their reliance on the technology is increasing. With this advancement, traditional methods to quality control inspections fall short. Traditionally, manufacturers include one or more steps to inspect products for defects visually. The visual inspection process is typically highly manual, making it vulnerable to human error and highly time-consuming. Moreover, traditional machinery used in machines are not flexible enough to adapt to product changes and can only detect a handful of defects at any time. Source: Google Cloud Artificial intelligence then is an agent that manufacturers are hopeful will bring in a more significant wave of innovation. Google Cloud listed multiple benefits of utilising AI, ranging from the reduced cognitive load for operators, fewer missed defects, no programming required (making it more flexible than previous machines), and the ability to detect hundreds of areas of interest on a product in seconds. Google’s new solution As per Kyocera Communications Systems, a major manufacturer of mobile phones for wireless service providers, Visual Inspection AI is an innovative service that non-AI engineers can use. Google Cloud says that its new Visual Inspection AI meets the needs of quality, testing, manufacturing, and process engineers who might not be well-versed in AI despite being experts in their respective fields. Thus, the new tool paves the way to many substantial benefits compared to general-purpose machine learning (ML) models, such as superior computer vision technology, shorter time-to-value and high scalability. Through this, customers can deploy solutions within weeks, and an interactive user interface guides them through the steps. Visual Inspection AI has also improved accuracy by up to 10 times from general ML approaches. Finally, Visual Inspection AI deep goes beyond simple anomaly detection. Instead, it allows customers to train models that detect, classify and locate multiple defect types in a single image—doing so provides follow-up tasks on production lines to be automated. Source: Google Cloud There are multitudes of ways in which businesses can use Google Cloud’s Visual Inspection AI in manufacturing. Automotive manufacturing, for one, can use it for paint shop surface inspection or press shop inspection—to look for scratches, dents, cracks or staining. On the other hand, electronics manufacturing could employ the tool for defects in printed circuit board components, and general-purpose manufacturing could improve upon procedures like packaging and label inspection, fabric inspection, metal welding seam inspections—to name a few. As per the above mentioned Google Cloud survey on manufacturing trends, the most common roadblock to AI integration is the lack of talent to leverage AI properly. Given this, Google Cloud’s new Visual Inspection AI appears as a brilliant step towards the proper deployment of artificial intelligence in the manufacturing industry.
In 2019, Google Cloud identified six sectors as vital components of its growth: public, healthcare, financial services, retail, media, and manufacturing. Within manufacturing, the cost of quality control and inspection continues to be among the highest. The American Society for Quality estimates that the price of quality may be as high as 15 to 20 […]
Powered by Google Cloud’s computer vision technology, Visual Inspection AI will help businesses to reduce quality control costs.
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["machine learning", "artificial intelligence", "AI", "cloud computing", "ML", "computer vision", "RAG", "Aim", "anomaly detection", "analytics"]
["AI", "artificial intelligence", "machine learning", "ML", "computer vision", "analytics", "Aim", "RAG", "anomaly detection", "cloud computing"]
I am an economics undergrad who loves drinking coffee and writing about technology and finance. I like to play the ukulele and watch old movies when I'm free.