In this era, people using vehicles is getting increased day by day. As pedestrians leading a dog for a walk, or hurrying to their workplace in the morning, we’ve all experienced unsafe, fast-moving vehicles operated by inattentive drivers that nearly mow us down. Many of us live in apartment complexes or housing neighborhoods where ignorant drivers disregard safety and zoom by, going way too fast. To plan, monitor and also control these vehicles is becoming a big challenge. In the article, we have come up with a solution to the above problem using the video surveillance considering the video data from the traffic cameras. Using computer vision and deep learning technology we will be able to recognize violations of rules. This article will describe modern CV and DL methods to recognize vehicle on the road and traffic violations of rules by them. Implementation of methods can be done using OpenCV Python as a tool. Our proposed solution can recognize vehicles, track their speed and help in counting the objects precisely.

The article describes problems of determining the type and automatic sorting of household waste using mobile computing devices. All of the required hardware and partially software, required for implementation of this service, are already present in modern smartphones. iOS and Apple products were selected as the base for the service, due to such advantages over competitors: dual or triple depth camera (TDCS), powerful GPU, Neural Engine coprocessor, high autonomy (2750mAh battery size), sensors that allow for user positioning and navigation in space (GPS, Glonass, Gyroscope) and most important feature is possibility of cross-platform designing, suitable for iOS and macOS (Project Catalina). The recognition process consists of several phases, including capturing of graphic image and detecting the object shape, shape analysis, computing the results, and saving new associations to the database. The analysis itself is implemented using a neural network that is able to learn during its operation. Initially, the algorithm is driven by the selection of photographs with a certain type for the base set of associations, each subsequent scan improves accuracy. Cross-platforming plays a very important role — it allows us to develop a single software service that is initially run on a macOS-based computer for faster learning and then can be easily used on an iOS mobile device. After identifying a particular type of garbage, the route to the nearest recycling point of such type of garbage will be proposed for user or user’s clarification will be requested. User can also manually browse categories and related items, manually search by name of item, and view locations for sorting and recycling in appropriate city. When a completely unknown object arrives, it is possible to refine the information in order to help further learning of the network.

Unsuitable climatic conditions, various natural disasters and instability and unpredictability of the weather significantly complicate cultivation, and sometimes make it even impossible. To ensure the best conditions for cultivation and the highest yields, farmers began to use greenhouses. However, in our hectic lives, people are constantly busy with something and there is no enough time. Long trips, business trips, vacations are also possible. It is becoming increasingly difficult to provide the necessary conditions for plants to grow on their own. That is why the Internet of Things has been so successfully integrated with agriculture that it has led to the emergence of automated or intelligent greenhouses. The article attempts to analyze the types of greenhouse monitoring and control system, their technical characteristics, principles of operation and basic requirements for these systems. According to the results of the study, the best smart greenhouses have been selected. The main functions of automated greenhouses have been described. Selection criteria have been determined and a comparative analysis of the most popular products available on the market.