Definition of the problem
The technical analysis is the foundation of package inspection. It is important to identify the types of defects that the system needs to detect.
Questions to include:
- What is Packaging Inspection Environment?
- Is it better to have the inspection system deferred than in real-time?
- How often should defects in the inspection system be detected?
- Are there any solutions to package inspection or must we develop one from scratch?
- What system must notify you about the defects identified?
- Is it necessary for the system to keep track of detection statistics?
Prepare and gather data
Machine learning engineers have to collect and prepare data in order to train models. The data for Package inspection is all about video records. Visual inspection models with video frames are used to process images. There are many ways to gather data. However, the most commonly used methods include:
- Processing an existing client video.
- Use open-source videos to help you design your model.
- After completing deep learning model requirements, you will be able to collect data from scratch.
Quality is the key parameter. A higher quality Video Dataset will produce a more precise output.
After gathering all data, we prepare it to be modelled, checked for anomalies, and ranked according to their relevance.
Building a deep-learning model
The task, budget and delivery time of choosing a deep-learning model for development are limited.
1. Implementing a deep learning service (such as Amazon ML, Google cloud ML engine)
This is an option when defect detection features match templates in a selected service. You don't need to make new models because of the time and budget savings. Instead, you upload data and provide model options according the task that the model must do.
2. Use pre-trained models
It is a pre-built deep learning model that can do the tasks we need. This means that there's no need to build a model from scratch. It works with a trained model based on our data.
3 Deep learning model design from scratch
This is a unique approach for all package inspection systems. This model takes longer to build, but it is worth the effort. When developing a package inspection plan, machine learning engineers use many computer-vision algorithms. These include image classification, object detection and instance segmentation.
There are many factors that must be considered when developing a deep-learning model.
- Image resolution.
- Defect types.
- You can inspect many products.
- Lighting conditions for inspection environment
- Size of defects and items
- What are your business objectives?
Let's assume that we are working on a factory vision solution to detect anomalies when objects pass through a factory conveyor belt. A practical data base is needed for visual inspection. There are three types of defects: damaged package, package without seal, and open packaged. This is why it is so important to build a segmentation system from scratch. Sometimes, we can deploy a pretrained model.
Training and evaluation
Next, you will need to train the model. Machine learning engineers will then evaluate the performance and accuracy for the model. It is better to have a test set. The test collected Dataset For Machine Learning to inspect packages is, for example, a collection of video records that are similar to the training model.
Implement and improve
It is essential to ensure that your visual inspection model works with the appropriate hardware and software architectures before installing it.
Software
Package inspection software includes neural network processing and python framework. It also offers web solutions for data transmission. Data storage is a critical parameter in package inspection. Data storage can be done in three ways: server less architecture (cloud streaming), local server, or cloud streaming.
Each package inspection should include a video database. Data storage is dependent on deep learning model operations. If the system is using a lot of data, the best way to store it is to use a cloud streaming service.
Hardware
Camera: This camera is used for live streaming of CCTV and IP cameras.
CPU/GPU. If you are looking for real-time results, a GPU will be the best choice. For package inspection, we can use CPU but not for training. The optimal GPU for example is the nvidia Jetson nano.
Image Recognition And Annotation With GTS
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