Basically, what we’re doing here is using some fancy algorithms to look at pictures taken from drones (or planes or satellites) and figure out how many cars are parked in the parking lot of your local Walmart.

Now, you might be wondering why do we need to use low resolution images? Well, for starters, it’s cheaper and easier to collect data that way. Plus, sometimes you don’t have a choice because the drone is flying at a high altitude or there are other factors like weather conditions that affect image quality.

So how do we go about detecting vehicles in these low resolution images? Well, first we need to preprocess the data by converting it into grayscale and resizing it to fit our model’s input size requirements (which is usually 320×320 pixels). Then we apply some image augmentation techniques like flipping, rotating, or cropping to make sure that our model can handle different orientations of the vehicles.

Next, we feed this preprocessed data into a deep learning algorithm called YOLOX-X (You Only Look Once with eXtensions). This is basically a modified version of the popular YOLO (You Only Look Once) object detection framework that was originally developed for real-time object detection in images and videos.

The main idea behind YOLOX-X is to divide an image into smaller grids or cells, and then predict bounding boxes around each vehicle within those cells. The algorithm uses a combination of convolutional neural networks (CNNs) and regression techniques to estimate the position, size, and orientation of each vehicle in real time.

Now, you might be wondering how accurate is this method? Well, according to the authors’ experiments, YOLOX-X achieved an average precision (AP) score of 80% on a benchmark dataset called COCO (Common Objects in COntext). This means that it can accurately detect and count vehicles in low resolution images with high accuracy.

But what if we want to improve the performance even further? Well, one way is by using semi-supervised learning techniques like Consistent-Teacher, which was proposed by another group of researchers at Tsinghua University. This method involves training a model on both labeled and unlabeled data, and then using a consistency regularization technique to ensure that the predictions are consistent across different iterations of the algorithm.

In their experiments, Consistent-Teacher achieved an AP score of 83% on the COCO dataset, which is slightly better than YOLOX-X. However, it also requires more computational resources and training time to achieve this level of performance.

It’s not rocket science, but it does require some fancy math and computer programming skills!