Automated Visual Inspection Powered by AI in 2020

Concept of AI-based Visual Inspection

AI-based defect detection is an ensue of computer vision and deep learning (an aspect of machine learning). The machines learn through examples. They are provided with a neural network that possesses different sets of data. Upon implementation, the deep learning algorithm will allow the differentiation of characters and anomalies, which is similar to a human-based inspection system.

Let’s understand human Vs. AI-based visual inspection with an example.

There is a food-manufacturing unit that has employed a food inspector to keep a visual check on the process and machinery used to maintain the quality of the product. The job of the inspector is to notice anything out of ordinary such as a defect or a process gone wrong due to some machinery failure. Here the inspector will judge a situation based on his prior experience.

Now let’s consider a machine-based vision. It starts with data sets and once automation begins it mimics the same human-based inspection but with better capability. The machine’s deep learning, which is powered by a neural network allows it to make a decision based on its own learning during the process and is not influenced by prior experience.

The integration of an AI-based visual system in any business needs both hardware and software.

The hardware involves certain devices such as a camera for real-time streaming, GPU (does fast processing in case image-based deep learning model), and photometer & colorimeter for detailed visualization.

The software needs a Python framework for neural network & web solutions for data transmission. The data storage can be via local server or cloud streaming.

The Advantages Of AI-Based Visual Inspection

It commits a far greater efficiency
Visual inspection can be a tiresome job for humans as its repetitive in nature. Without a doubt, machines can work tirelessly without being influenced by emotions or needing to take a break. The output would be far greater than humans.

Negligible room for error
Once the data sets are good to go, it is highly unlikely for machines to make a mistake. But to err is human. Detecting the error and coming to a conclusion after hours/days of going through manuals and journals is subjected to mistakes.

Saves time and money
Absolute vitals for any business, AI-based visual inspections are the need of the hour. Finding the anomaly and reporting it will take far less time in case of a machine. You just to invest in automation setup once and enjoy the quick results.