YOLOv7 object recognition

1. Required skills:

- Basic knowledge of statistics and probability theory.
- Certain knowledge of the methodology of neural networks and elementary architectures, such as dense neural networks and convolutional neural networks (CNN = convolutional neural network).
- Knowledge of frameworks for manipulation of neural networks: for example, PyTorch or TensorFlow.

2. Data format.
Thermal imaging camera. Camera is connected via USB to the Jetson Nano (Ubuntu):
- input image size 256 x 192 pixels;
- each pixel is coded with one number - intensity within [0, 1].

For training, it is better to take an already trained neural network, add layers for resizing the image (at the beginning), and modeling the desired output (at the end).

Preparation of a training sample for training.

Here are a few steps. It is the most complex task:
(a) Acquisition of thermal images through the camera in their final form.
(b) Development of an algorithm for the synthesis of artificial images of the desired type with variations in the number, shape, and size of objects for identification. This algorithm should include a well-designed and controlled probability distribution, with the necessary parameters for image curation.
(c) Generating the required number of images.
(d) Realistic image noise with a separate ready-made algorithm, or with the algorithm and point (b).