With the help of neural networks, object recognition is finding its way more and more into different areas. Neural networks are based on the structure of the human brain. They consist of any number of connected neurons and perform mathematical operations.
The finished toolchain should be prepared for changing situations such as lighting, object size and type, and weather. This means that there are several options for different object trackers, segmentation algorithms, and machine learning models to switch on the fly if necessary.
Other fields of Application
However, this form of object recognition can be used in some areas. Still, no training data can be recorded via cameras, for example, in medical imaging procedures such as ultrasound, MRT, or PET-CT. Here, however, the basic concept can be used to annotate healthy and abnormal tissue structures with the help of live recordings. For example, medical staff could mark the tissue during routine examinations to generate data. These can then be used to train a model to support the detection of diseases later.
Conclusion
Training data generation with mobile devices has the potential to quickly and efficiently annotate new data when a new object recognition is to be developed. The age of training data still requires a lot of manual work. Although various partially automated algorithms support this, the degree of automation could increase with the described approach. Due to object trackers with higher performance in the future, segmentation algorithms, and more powerful end devices, this approach could be an alternative to conventional training data generation in certain areas.
Also Read: MLOps: Guide To Deploying And Monitoring ML Models
