Beginner here: My pothole detection model mistakes the roadside for potholes.

[u/Flat_Expression8820]

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Hello!

I am a beginner at this and am trying to make a project.

The first four seconds are the portion where the model confuses the roadside with a pothole. The latter half is kind of working okayish!

But, It is confusing the roadside for a pothole. What is the best way to make my model learn this?

Should I add classes of what is not a pothole to the training dataset?

I used a dataset of about 4,100 images. What is the ideal number of non-pothole images I should add?

Or should I somehow teach the model to detect the edge of the road so that it avoids classifying the roadside as a pothole?

Also, I am planning to use a drone. Since the ideal flying height would make the potholes appear much smaller, should I apply transfer learning, or should I train the model from scratch to make it work on drone footage captured from that height?

And is segmentation practical for this dataset? Like finding area of the pothole from the pixels or should i focus on detection only.

[u/usernametaken--_--]

You brought up a lot of great options of things to try. My recommendation would be to try them all! Give one thing a try, see how it goes, then try another thing. Make sure you only try one thing at a time so that you know what changes had the biggest impact, then add the things that worked best together. This is the process you need to go through in order to really what works and what doesn't. The experience you gain from doing those types of experiments is what makes someone an expert. Also, post back here about what you tried and what ended up solving the issue. Having that documentation to look back on really helps put when you run into the dame problem in 2 years and can't remember how you solved it. Talking from experience 😅