Method of integral transformations in solving the problem of detecting small vehicles in images

The article proposes a method for forming a set of features on images based on three integral transformations: the Radon transform, the Steklov function and the Fourier transform. Using a discrete analog of these transformations, the values that form a set of features are calculated. Depending on the transformation parameters, the number of features can be changed. We selected the values of these parameters such that the number of features is 903. The use of this approach in solving the problem of detecting small-sized, and therefore poorly visible, vehicles in video images is shown. In addition, we have developed an improved version of the least squares method based onprocessing the obtained features using some transformations of the labels of the training set of images. The main essence of this method is to perform an affine transformation of the label value into a small neighborhood of its original value. A priori estimates show a decrease in the approximation error using the least squares method. The paper also shows a comparison of the developed approach with convolutional neural networks. This comparison allows us to say that it is not much inferior to them in such an indicator as the percentage of correct predictions. At the same time, in terms of prediction execution time, the method presented in the article works 3-4 times faster depending on the model used. In the practical part of the work, software tools from the OpenCV, Keras and Scikit-learn libraries were used.