# Influence of Training Parameters on Real-Time Similar Object Detection Using YOLOv5s
## 1 Introduction  
Artificial intelligence has become one of the main areas of interest in recent decades. A large amount of data is collected in various fields that can be used to train models to solve different problems, such as creating recommendation models; personalizing systems for our needs; developing the Internet of things; and securing our devices, etc. The field is broad, but image recognition has received special attention because of its wide applicability in practice. In medicine, image recognition is widely used for various diagnoses of diseases--for example, for different types of cancer [1; 2; 3]. In the military, drones are used to patrol and recognize objects, such as smugglers [4]. In autonomous vehicles, image recognition helps to analyze road activity and recognize traffic signs [5; 6]. Real-time object detection also helps in agriculture, for example, by identifying and preventing various plant diseases [7; 8; 9] that help to save a harvest or implement the new technologies that today lead to the success of modern agriculture. Today, common computer vision tasks include image classification and localization, object detection, semantic segmentation, instance segmentation, and classification. In this paper, real-time object detection was analyzed. Currently, one of the main problems of object detection is the collection and preparation of data and their use in training models, which largely determines the accuracy of the trained model. Despite the different fields of application, the problem of detecting similar objects is often encountered, which is attempted to be solved by applying various methods and algorithms of object detection [10]. Detecting similar objects involves problems of color, size, curvature, or other similar features. As a result, the accuracy of the models deteriorates, so it is important to fully examine the effectiveness of object detection algorithms and data preparation for it [11]. Object detection is one of the main topics in the field of artificial intelligence. Many researchers are studying and trying to obtain more accurate recognition results, for example, in self-service checkouts for item recognition [12] and in medicine for the recognition of certain diseases [13; 14]. However, some researchers focus on objects that are large enough and have different appearance characteristics [15], whereas others focus on small objects [16]. During experiments, large and different objects are usually divided into five or more classes, while small objects are divided into 1-2 classes. The biggest challenge is when the objects are very similar in color, shape, size, and other appearance characteristics. For example, apples are similar in shape but different in variety, so it is important to pay attention to the color. However, the model has distinguished between 10 and more different classes of very similar and small objects. Many object detection models have been developed and published publicly, but generally not many of them focus on similar object detection. The novelty of this article is that the main focus is on the real-time detection of similar objects using the mobile device. Such a method of object detection leads to different challenges. For this purpose, the new similar object dataset of the construction details has been prepared and analyzed. In total, the detection of 36 classes (construction details) has been analyzed. In this paper, similar data are considered objects that, in some cases, can look identical. For example, the construction details can look identical, depending on the angle of the camera in real-time object detection. An example of similar objects is presented in Figure 1. From the top view, these details look the same. It is obvious that object detection results depend on various factors, such as the selected object detection algorithm and its hyperparameters, as well as image properties that have been used to train algorithms.