How to Integrate AI Visual Inspection System
- STATE THE PROBLEM
Visual inspection development often starts with a business and technical analysis. The goal here is to determine what kind of defects the system should detect.
Other important questions to ask include:
What is the visual inspection system environment?
Should the inspection be real-time or deferred?
How thoroughly should the visual inspection system detect defects, and should it distinguish them by type?
Is there any existing software that integrates the visual inspection feature, or does it require a development from scratch?
How should the system notify the user(s) about detected defects AR glasses?
Should the visual inspection system record defects detection statistics?
And the key question: Does data for deep learning model development exist, including images of “good” and “bad” products and the different types of defects?
Data science engineers choose the optimal technical solution and flow to proceed based on the answers they receive.
AI Application Development Guide For Business Owners
- GATHER & PREPARE DATA
Data science engineers must gather and prepare data required to train a future model before deep learning model development starts. For manufacturing processes, it’s important to implement IoT data analytics. When talking about visual inspection models, the data is often video records, where images processed by a visual inspection model include video frames. There are several options for data gathering, but the most common are:
Taking an existing video record provided by a client
Taking open-source video records applicable for defined purposes
Gathering data from scratch according to deep learning model requirements
The most important parameters here are the video record’s quality. Higher quality data will lead to more accurate results.
Once we gather the data, we prepare it for modeling, clean it, check it for anomalies, and ensure its relevance.
- DEVELOP DEEP LEARNING MODEL
The selection of a deep learning model development approach depends on the complexity of a task, required delivery time, and budget limitations. There are several approaches:
3.1 Using a deep learning model development service (e.g: Google Cloud ML Engine, Amazon ML, etc.)
This type of approach makes sense when requirements for defect detection features are in line with templates provided by a given service. These services can save both time and budget as there is no need to develop models from scratch. You just have to upload data and set model options according to the relevant tasks.
What’s the catch? These types of models are not customizable. Models’ capabilities are limited to options provided by a given service.