Artificial intelligence is a key differentiator for businesses, with numerous applications in almost every domain. From self-driving cars to Siri and Alexa, AI-based visual inspection is the key enabler for next-generation services transforming the way we live.
Automated visual inspection techniques can help save your business time, effort, and money. Read on to discover how automatic visual evaluation and a deep learning approach can save significant time and effort.
What is AI-Based Visual Inspection?
Visual inspection is one of the most commonly used approaches in the production process. It entails visually inspecting the components of an assembly line to detect and repair problems.
However, when describing AI-based visual inspection, it’s frequently about some form of optical inspection technique based on deep learning and computer vision. It is the process of monitoring and inspecting a manufacturing or service operation to ensure that products meet predetermined specifications.
A computer is used to capture, record, and store images as well as objects. Thus, it saves time and also increases efficiency. For example, if an inspector inspects an assembly line, it may take him/her a couple of hours to finish the inspection process, whereas an AI-powered software will scan the assembly line within a few minutes.
What is Deep Learning in a Visual Inspection?
Deep learning technology is becoming more and more popular for use in various industries. Its primary benefit is allowing machines to learn by example rather than explicitly program. Doing this makes it a powerful tool for tasks that are difficult to automate, such as visual inspection.
The basic principle of deep learning is to teach a machine to recognize specific patterns by providing a neural network with labeled examples. Once the device has learned those patterns, it can apply them to new data to identify the defects.
Integrating deep learning algorithms with automated visual inspection technology allows discriminating components, abnormalities, and characters, simulating a human visual examination while running a computerized system.
Limitations of Manual Testing
Many companies rely on manual testing as their only quality control measure, but this approach has limitations. Let’s explore some of these limitations:
1. Occasionally Hazardous
Not every time the defect detection entity is safe to inspect. When assessing elements like baggage screening or aircraft maintenance, there are multiple risks involved to inspect such entities under normal conditions.
2. Time Consuming
In property and casualty businesses, studying and assessing the damage to a building or automobile usually takes time. Hence, the inspection and claim settlement process is quite lengthy. Because most of these activities or scenarios are done repeatedly, manual testing takes considerable time.
In such cases, computer vision can significantly speed up the process, minimize mistakes and prevent fraud. Moreover, you can use satellite imagery, drones, and big data to do these computer-assisted inspections.
A typical machine learning application analyzes behavioral data such as facial expressions or voice tone during underwriting. For example, in the case of health insurance, it is projected that behavior monitoring will provide over 40% of risk information.
Additional Read – 9 Real-World Problems Solved by Machine Learning
However, behavioral data is also essential in non-life insurance. For instance, identifying particular trends in how a person runs a machine may suggest process problems resulting in insurance claims.
Manual inspection is prone to making two forms of mistake, either failing to detect the error or identifying the defect which doesn’t exist. This ineffective visual detection can lead to ineffective estimations and a waste of employee efforts.
4. Human Vision is Unreliable.
Optical illusions are an example of how untrustworthy the human eye can be. Moreover, when comparing two similar objects with small dimensions, there are chances that the human eye will fail to recognize the slight difference in measurements. It isn’t to say that manual examination is useless; it indicates that relying solely on it isn’t a good idea.
5. Subjective to Inspector
The manual testing procedure is inconsistent since each individual’s testing methods, and tactics vary. Because this yields ranged results on the same test, variance in the test method is unavoidable.
6. Impractical Performance Testing
Performance testing of any client-server application necessitates the use of humans and computers. Client programs must be installed on several PCs and tested by a single person to determine the overall performance of the software, which is a time-consuming and challenging job.
7. Cost of Labor
As individuals on a large scale cannot handle quality inspection, companies tend to hire multiple skilled trainers, and hence, the manual examination remains a costly endeavor. According to Glassdoor, manual inspection operators may earn anywhere between $50,000 and $60,000 per year.
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