Machine learning advantages and disadvantages

Neural networks offer several advantages over traditional machine vision algorithms. They can adapt to changing input data due to their learning capabilities, making them more robust and reliable under varying conditions such as lighting, scale, and object orientation. Neural networks are also more accurate in detecting complex or irregular patterns, like reflections, defects, or textures.

Unlike traditional methods that require expert programming, neural networks simplify the process for users by allowing model training through labeled datasets, reducing the need for coding expertise.

However, neural networks come with disadvantages: they require powerful and expensive hardware, significant amounts of high-quality labeled data, and careful tuning of training parameters. Poor data quality or biased datasets can lead to incorrect results. Additionally, software licenses for neural networks tend to be more expensive than those for traditional vision systems.