Machine learning and Artificial intelligence algorithms in identifying and classifying Airplane parts

Question

Airplane parts and functions

Can Machine Learning and Artificial intelligence Algorithms assist in identifying and classifying Airplane images parts?

Answer 1

THEY SHOULD

Image identification is already a major topic in machine learning, as a subset of problems with categorical outcomes. Such problems often get called classification problems, but this is really somewhat poor terminology, as the standard statistical models used actually do no classification. The typical approach is to predict class membership probabilities. If a step downstream of that wants to use those probabilities to predict the categories, such an extra step can be used but should take into consideration the costs of making mistakes (mixing up the airplane parts). This answer gets into the statistical and decision components being separate, and an answer to my question here by that same Stephan Kolassa discusses some other aspects of working with the probability values instead of just the categories and why, despite how common it is to make a categorical prediction based on the category with the highest probability, this might not be appropriate for all tasks. Yet a third answer of his discusses what is happening in problems with clear distinctions between outcomes (“high singal-to-noise ratios”), which might be the case for your problems involving aircraft parts (but maybe not). Such a situation might make it rather reasonable to take the category with the highest predicted probability, though it is useful to know you do not have to do this and that typical models do not explicitly predict categories.

The simplest statistical models for categorical outcomes are logistic and multinomial logistic regressions. For image recognition in particular, models known as convolutional neural networks (CNNs) are popular. A YouTube video by Brandon Rohrer was one of my first exposures to CNNs, and he has an expanded video. I also find it useful to think about CNNs as I draw here.

A first task, often described as the “Hello world” of image recognition, is to work with a dataset called the MNIST handwritten digits. You can find discussions about this data set all over the Internet and might consider working with it to introduce yourself to image recognition.

I suspect that techniques from image recognition and computer vision machine learning are already used for this kind of part identification for aircraft and other vehicles. There should be academic articles that explore such ideas. You also have included the relevant classification, computer-vision, and image-processing tags. It might be worth clicking on those to read the high-vote questions and answers.