YOLO- NONE object detection from thermal images

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1

Hi there!

I am a Mining Engineering student. I am working on the research which is focused on AI-Driven Frameworks for Mitigating Rockfall Hazards. It is about detection of loose rock through thermal images in underground mining to prevent falls or other unfortunate situations.

I found out that YOLO can detect not just with shapes but also with colors in the pixels. Detection of traffic light, or fire can be as a prove.

Thermal images in underground mining also consist of RGB colors, for example, blue is unstable rock, which might fall, red is stable rock and so on. Every time the shapes of the unstable rock will be different, can YOLO still detect the unstable rock or blue color? And how accurate it can be? Thermal image will be taken with UAV drones.

I tried couple of tests, first made an annotation, then trained the YOLO, but when I tested with another thermal image and video, YOLO didn’t detect anything. Of course, it needs more training and more images. But I wanted to make sure that I am on the right way, or maybe some other additional methods would work with thermal images.

I appreciate if someone could share information about that.
Thanks)

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It uses color. So as long as you have enough data, it should work.

3

Like Toxite mentioned, it should work. The key is that you’ll need a lot of images and annotations to get a result that will generalize well (meaning it works on data it’s never seen before). You’ll also need to be consistent with your annotations (make sure to label all instances of a class) and keep tight bounds around each object.