The question at the heart of this is one we hear constantly: how do you move from structured data to something as visually complex as defect detection? The answer, as with most meaningful technical leaps, is not that it is impossible. It is that the path is different from what your machine learning background has prepared you for, and that difference is not a barrier but a map. The person asking this is not behind. They are standing at a natural junction, and the first step is not to learn everything about convolutional neural networks or image segmentation. It is to acknowledge that your structured data skills are a foundation, not a limitation.
What makes this transition manageable is that the core discipline remains intact. The workflow you already know, the discipline of cleaning data, engineering features, and iterating on model performance, carries over. What changes is the raw material. Images are not rows in a table. They are grids of pixels, and the patterns you need to find, like a scratch on a machined part or a discoloration in a fabric weave, are not expressed in a single column. They live in the relationships between neighboring pixels, in edges, textures, and contrasts. This is why the advice to start with structured data is so sound. You are not abandoning your expertise. You are extending it into a new modality, and the learning curve is steep but not vertical. The difficulty is not in understanding the math. It is in learning to think about data as spatial rather than tabular.
For someone with your background, the practical starting point is not a textbook on advanced computer vision. It is a curated set of images that represent the problem you want to solve. Find or build a small dataset of product images, some with defects, some without. Then, use a pre-trained model like a convolutional neural network from a library such as PyTorch or TensorFlow. You do not need to build an architecture from scratch. You need to learn how to fine-tune one. This is the equivalent of using a library for regression instead of writing the linear algebra by hand. The reference you need is not a paper on attention mechanisms. It is a tutorial on transfer learning with image classification. Once you see that a model can be trained to distinguish a defective object from a clean one with a few hundred examples, the mental model clicks. The complexity does not disappear. It becomes manageable.
The real insight here is that your question reveals a common misconception: that computer vision is a separate discipline requiring years of dedicated study. It is not. It is a set of tools, and the tools have matured to the point where a practitioner with solid machine learning fundamentals can pick them up in weeks, not years. The difficulty is not in the models. It is in the data preparation, the labeling, and the evaluation of visual results. These are tasks your structured data experience has already trained you for. So start with the data you have, or the data you can collect. Build a small, ugly prototype. Then iterate. The leap is not from structured data to computer vision. It is from thinking in columns to thinking in pixels, and that leap is made one image at a time.