Improving a model of object recognition in images based on a convolutional neural network

Abstract

Image processing is extremely important in modern science and practice, so it is constantly evolving and improving. Image processing can be used in many industries, namely precision farming (agricultural monitoring), safety systems, quality control, etc. The given areas employ vision systems, robotic complexes, unmanned aerial vehicles (UAVs), video surveillance systems, web services, and mobile applications for identification and search. One type of image processing is the recognition of objects in images, which is widely used in the industry, art, medicine, space technology, process management, automation, and many other fields [1]. Recognition of objects in images involves class attrition of the source data to a certain class by highlighting significant features. These attributes characterize the initial data from the general array of non-essential information. There are many methods for recognizing objects in images, among which Random Forests techniques, boosting methods, as well as neural network procedures, specifically convolutional [2–6], are the most common. Certain requirements are put forward for object recognition methods, namely: – correspondence of the recognized object to the real object; – high performance; – resistance to errors; – high accuracy. Therefore, it becomes necessary to analyze the methods of object recognition in images and to choose the optimal according to the above requirements, specifically high accuracy. It is also worth considering the parameters that characterize these methods, changing which directly affects the precision, performance, and overall efficiency of the process of object recognition. A modern relevant industrial area is the development of precision agriculture, which is based on the results from agricultural monitoring. These data, acquired from UAV video cameras, make it possible to assess the harvested crop, control the routes of movement of agricultural machinery, predict yields, etc. In this case, an important criterion is the UAV’s ability to avoid collisions with close objects, determine the position in space, direction, and trajectory of the flight by receiving input data on the recognized objects. The effectiveness of these systems is determined by the precision of object recognition whose evaluation requires experimental research.