# Application of Deep Neural Networks in Recognition of Selected Types of Objects in Digital Images
## 1 Introduction  
Artificial intelligence is a rapidly developing IT direction. Nowadays, it is becoming an inseparable part of human needs and conveniences. The constant demand for more and more abstract automations causes an increase in supply from corporations providing software--currently, most often in the form of software based on artificial intelligence algorithms. With each subsequent year of development, this area covers more and more areas of modern life. Today, AI is used, among others, in medicine, automotive, mobile devices, tourism, or agriculture. Good examples include airport baggage checks [1], facial recognition [2], handwriting recognition [3], stock market forecasts [4], and even weather forecasts [5]. There are many possibilities of creating or using an already trained model based on machine learning algorithms. However, the use of different learning techniques, different network architectures, and the appropriate selection of parameters may turn out to be different depending on the type of data that our algorithm is supposed to process. For example, to create a model that is supposed to process digital images and classify objects on it, it will be beneficial to use, among others, convolutional networks (CNNs), because these networks allow for the detection of increasingly complex patterns and features in the input data. Thanks to the use of a structure inspired by the biological visual cortex, convolutional networks can effectively analyze images by gradually processing information on successive layers, which leads to a reduction in the number of parameters and improved efficiency compared to traditional neural networks [6]. Another example is natural language processing and the process of generating new text based on existing text data or templates. In this case, the approach based on recurrent neural networks (RNNs) is popular, becausetheir architecture allows for processing sequential data and learning the relationships between these sequences [7, 8]. Although convolutional networks are currently mainly used for detecting objects in digital images, there are still different approaches that can be used in this aspect. For example, when creating a model, different architectures, numbers of layers and parameters are used, which can be adapted to a specific task. Some approaches will generate better results in terms of precision, accuracy, and sensitivity, but require more time to train the model or the matching process. Other approaches are faster, but may lead to worse results. New architectures are constantly being developed, which are characterized by increasing accuracy of the work performed. From 2010 to 2017, annual competitions were held for classification of 2D objects from the ImageNet database [9]. To win, the neutral network had to achieve the lowest possible error level during classification of test data. The AlexNet and GoogLeNet architectures discussed in this paper won in 2012 and 2014 [10], respectively. In the case of detecting objects in digital images, different techniques are also used. One such approach is to combine convolutional networks with region proposal networks (RPNs). This approach can significantly speed up the learning and matching processes, because the image regions where the potential object is most likely to be located are checked [11]. Due to the existence of many possibilities for constructing an artificial intelligence model, questions may arise regarding the optimal selection of the network architecture and parameters for a given type of data and what may affect such a choice. In response to these and other questions, a research project was undertaken. An additional motivation to conduct the research presented below were three articles. The first article [12] focuses on defining the advantages and limitations of using a given machine learning technique. The work is a kind of comparative review of a large number of scientific works related to the machine learning techniques specified in the title.