# Approach to Automated Visual Inspection of Objects Based on Artificial Intelligence
###### Abstract
Within artificial intelligence, we distinguish a large number of algorithms and principles of operation of individual approaches, such as machine learning and its subgroups, e.g., deep learning. Their most striking advantages are especially visible in areas where a huge amount of data needs to be processed. This can be paradoxically considered as an advantage for such systems due to the need for a large amount of data in order to learn more accurately and the subsequent improvement of system decision-making capabilities [9]. The use of deep learning is in areas such as automatic text translation and generation, colorization of black and white images, automatic driving tasks for various autonomous vehicle systems, online self-service solutions, classifying images of various types, robot control and so on. Due to the aforementioned properties of deep learning, its use proves to be very suitable also in the field of visual inspection of products. Visual inspection is one of the basic and fastest methods of non-destructive quality evaluation of products and testing of materials, and due to its simplicity, it is usually performed as a first method for testing [7; 10]. The basic type is direct visual inspection, where the surface is examined either with the naked eye only or with the help of a magnifying glass. Indirect visual inspection can be used for elements and places with complicated access if needed. In both basic ways, the person evaluates the image and then evaluates the result of the inspection using subjective opinion. Due to the disadvantages of such an inspection, which are monotony and the subjective evaluation of the results is an attempt to automate these processes [11; 12]. In various fields, the common approaches to evaluating the acquired image is sufficient, e.g., comparing reference images with inspected ones [13], searching for elements using the speeded-up robust features method, and many others. However, the aim of this research is to automate the areas of visual inspection, in which the searched elements occur randomly, and have different shape, dimensions, colour and location. In such cases, the use of artificial intelligence and, in particular, neural networks seems to be a promising solution as a replacement for human decision-making ability. The deep learning method is widely used, mainly for analysing obtained 2D visual data [14; 15], both X-ray and shearography. Both of these methods offer a means of obtaining data from surface or internal structures of inspected material. In the case of shearography, it is possible to find defects on the surface or bubbles below the surface [16; 17]. Radiography is an appropriate method for detection defects inside the material different to rubber for instances metallic belts, bead wire etc. [18; 19; 20; 21]. Every mentioned method has own advantages and disadvantages. Visual inspection based on cameras has advantages in simple using and low price requirements in comparative to radiography. The disadvantage lies in the impossibility to detect defects in the internal structure of a tire such as metallic belts. This paper proposes an efficient defect detection method suitable for tire quality assurance, which takes advantage of the feature similarity of tire images to capture the anomalies. The condition of car tires appears to be one of the important factors determining the occurrence of accidents so the tire quality can be considered crucial for safety in transport systems. Therefore, several authors [22; 23] are focusing on evaluation and testing of tires, their mechanical properties or pressure distribution in the tire-ground contact patch via the camera system. However, they do not focus on production inaccuracies, surface quality or other kinds of defect. Automatic defect detection is an important and challenging problem in industrial quality inspection. Therefore, we looked for studies focusing on inspection systems in different areas as well, e.g., [14; 24; 25].