AI can enable systems to make intelligent decisions based on past data, from deciding which products customers might like best to identifying potential medical problems before they escalate into emergencies. Among this wide range of AI applications around the globe, automated visual inspection is highly appreciated.
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.
Below are the five steps to follow while integrating an automated visual inspection system:
1. State the Problem
It is essential to understand that the goal of the inspection is not to find all possible defects but to determine what kind of defects the system should detect. These are the defects that affect quality, safety, and reliability so that the customer can identify and care about them. To help you with the same, here are the essential steps to follow while identifying the actual problem statement for integrating automated inspection in manufacturing:
- Identify your system environment.
- Define whether the detection is to be real-time or deferred.
- Identify system notification when the defect is detected.
- Check whether you need to develop the new system from scratch or your default system enables the defect detection functionality.
2. Gather and Prepare Data
As an engineer in the data science field, you must prepare and gather the required data sets before deep learning can begin. For manufacturing industries, it’s important to digitize the product supply chain through IoT analytics. For instance, if we are talking about video records, the data preparation can include extracting frames from videos and creating bounding boxes on relevant objects on these frames.
There are many ways to collect the dataset; however, below are some of the standard methods:
- Utilizing video records provided by a client
- Engaging in open-source video recording applicable for a defined purpose
- Collecting data from scratch according to deep learning model requirements
After obtaining the data, we make sure it is orderly and ready to be modeled. Any anomalies explicitly related to this are checked for before proceeding.
3. Develop Deep Learning Model
In this stage, you identify the perfect deep learning model depending on the complexity of your system, budget limitations, and time constraints. Below are some of the common approaches:
- Model Development Services [Such as Google Cloud ML Engine, Amazon ML]
This approach is based on feature engineering. You are provided with the set of heuristic rules that experts in the field specifically derived to detect objects in images. This type of model is beneficial when the requirements of defect detection features are in line with the templates provided by the service. Doing this can save time and budget as there is no need to develop the model from scratch.
- Using Pre-trained Models
A pre-trained model is a deep learning model that has previously been constructed and performs tasks similar to those you want to complete. Pre-trained models may not always succeed on all of our tasks, but they offer significant time and cost savings. Using models previously trained to solve large datasets allows us to customize them for our needs.
- Deep Learning Model Development from Scratch
When developing the custom deep learning model from scratch, a data scientist should consider using many computer vision algorithms, for example, image segmentation, object detection, etc. This method is ideal for complex, secure inspecting systems. The approach may be time and effort-intensive, but the results are worth it.
For instance, consider an automated visual inspection system for assessing the automotive parts that detect scratches on the metal surface. After training the system, it can accurately detect all kinds of dents and scratches. In such cases, you don’t need to develop a completely different model and instead collect the images depicting defective, unacceptable parts.
4. Train & Evaluate
After developing the visual inspection model for your system, now it’s time to train it. Here, the data scientist has to test and evaluate the performance of your system and its result accuracy. Test dataset may be anything that can support the automated visual inspection system; it may be a set of video records that we are processing.
5. Deploy & Improve
Once you evaluate your model, it’s time to deploy and inspect it daily. Instead of directly applying your model on a large scale, you can test it on some of your products and identify its accuracy. If it satisfies the requirements you are looking for, you are good to integrate it with your entire system. Also, it is recommended to regulate your model quickly using the new dataset and trends available in the market.
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