Intelligent Traffic Monitoring through Heterogeneous and Autonomous Networks Dedicated to Traffic Automation.

In direct line with the evolution of technology, but also with the density of vehicles that create congestion and often road accidents, traffic monitoring systems are parts that integrate intelligent transport systems (ITS). This is one of the most critical elements within transport infrastructures, an aspect that involves extremely important financial investments in order to collect and analyze traffic data with the aim of designing systems capable of properly managing traffic. Technological progress in the field of wireless communications is advancing, highlighting new traffic monitoring solutions, and the need for major classification, but proposing a real-time analysis model to guide the new systems is a challenge addressed in this manuscript. The involvement of classifiers and computerized detection applied to traffic monitoring cameras can outline extremely vital systems for the future of logistic transport. Analyzing and debating vehicle classification systems, examining problems and challenges, as well as designing a software project capable of being the basis of new developments in the field of ITS systems are the aim of this study. The outline of a method based on intelligent algorithms and improved YOLOv3 can have a major impact on the effort to reduce the negative impact created by chaotic traffic and the outline of safety protocols in the field of transport. The reduction of waiting times and decongestion by up to 80% is a valid aspect, which we can deduce from the study carried out.