# A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt
## 1 Introduction  
Object detection is one of the fundamental tasks in computer vision. It deals with detecting instances and location in an image of certain objects. Later, detected objects can be classified into different categories (such as face [1, 2], traffic signs [3, 4, 5], vehicles [6, 7], pedestrians [8], human pose estimation [9], disease detection in various images [10, 11, 12], etc. ), segmented [13, 14, 15], captioned [16], tracked [17], and suchlike. In recent years, deep learning, described as representation learning involving a hierarchy of features, has rapidly advanced object detection and its implementation in several fields, such as computer vision (CV) [18], voice recognition [19, 20], natural language processing [21], remote sensing, human motion tracking [22], and medical applications [23]. In particular, remote sensing has many specific challenges involving sensors and software, and inevitably uses approaches similar to CV, such as statistical analysis, synthesis, and deep learning [24]. Object detection in natural images, which contain many different objects, is a central and highly challenging task in the CV field [25]. Deep learning contains a large field of algorithms and methods to solve various tasks. For example, convolutional neural networks (CNN) are very commonly applied for image analysis [26, 27]. The accuracy of CNN classification depends on several factors, such as data set, optimization method, and structure of the neural network [28]. CNN-based current object detectors are capable of detecting very small objects in a provided image [29, 30]. Recently, interest has increased for applications using graph convolutional networks and meta-learning. Graphs naturally appear in numerous application domains, ranging fromsocial analysis to bioinformatics to computer vision [31; 32]. Graph convolutional networks preserve data properties, such as structure and information [33]. Meta-learning is essentially an ensemble of learning algorithms for the task of learning to predict data. Meta-learning has been successfully applied for few-shot learning problems [34; 35]. However, such neural network structures require high computational capacity and are not easy to implement in today's embedded systems for practical real-time applications [36]. In their paper, [PERSON] et al. proposed a scaled version of a CNN architecture [37]. This architecture is called InceptionV3. InceptionV3 and its modifications are used in a wide number of applications as a backbone for classification, object detection, etc. [38; 39]. Another popular deep learning implementation, called SSD, was proposed by [PERSON] et al. [40]. It uses a single deep neural network and discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. SSD has two components: a backbone model and a head. The backbone model is usually a pre-trained image classification network [41], the head is usually a stack of convolutional layers on top of a backbone model. SSD network architecture is simple to understand and easy to implement [42]. Another popular object detection algorithm, which is easy to implement in embedded systems, is Yolo, proposed by [PERSON] and [PERSON] [43]. Yolo algorithm and its modifications have been successfully applied to many tasks that require object detection in real time [44; 45; 46]. In theory, currently developed deep learning methods allow us to detect any object in an image. However, in practice, small and dense objects still pose a challenge; e.g., [PERSON] et al. found that current object-detection techniques are not sufficient for detecting printed circuit board (PCB) components [47]. Moreover, such methods are not as efficient as expected when integrated into the manufacturing process. Therefore, some research works offer implementation designs [48]. In industry, the general goal is to speed up the manufacturing process and reduce the size of a product [49].