This is important for all sorts of reasons, like monitoring traffic or keeping an eye on crops. But how do we actually make this happen?

First off, there are a few different ways to approach this problem. One popular method is called “deep learning”, which involves using fancy algorithms and lots of data to teach computers how to recognize objects from images. This can be really effective, but it also requires a lot of resources (like time and money) to train the models properly.

Another technique that’s gaining popularity lately is called “semi-supervised learning”. Basically, this involves using both labeled data (where we know exactly what objects are in an image) and unlabeled data (which just shows us a bunch of images without any labels). By combining these two sources of information, the model can learn to recognize new objects more accurately than if it were trained on only one type of data.

One example of this technique is a paper called “Omni-DETR: Omni-supervised object detection with transformers”, which was published in 2022 at the Conference on Computer Vision and Pattern Recognition (CVPR). This model uses both labeled and unlabeled data to learn how to detect objects from images, and it’s been shown to perform really well on a variety of tasks.

Another interesting paper is called “Improved YOLOX-X based UAV aerial photogrammetry object detection algorithm”, which was published in 2023 in the journal Image and Vision Computing. This model uses deep learning techniques to detect objects from images taken by drones, which can be really useful for things like monitoring crops or inspecting infrastructure.

But what about when we don’t have a lot of labeled data? That’s where “consistent-teacher” comes in this technique involves using multiple models (each with different levels of accuracy) to teach the final model how to recognize objects more accurately. This can be really helpful for tasks like object detection, which often involve lots of false positives and negatives.

One example of this technique is a paper called “Consistent-Teacher: Towards reducing inconsistent pseudo-targets in semi-supervised object detection”, which was published at CVPR 2023. This model uses multiple teachers to learn how to recognize objects from images, and it’s been shown to perform really well on a variety of tasks even when there’s not a lot of labeled data available.

So that’s the basics! If you want to dive deeper into this topic (or if you just like reading academic papers), I highly recommend checking out some of these resources. And who knows, maybe someday we’ll all be flying around in drones and using fancy algorithms to detect objects from up high!