VEHICLE DETECTION ALGORITHM ANALYSIS: A SURVEY

Deep Learning is a rapidly advancing field that has the potential to revolutionize numerous areas of research and industry. One critical task within this domain is vehicle detection, which has practical applications in domains such as traffic management, public safety, and autonomous driving. Intelligent Transportation Systems (ITS) can be used for vehicle detection to count and track vehicles, detect incidents, and collect tolls. This helps improve traffic management, monitor flow and congestion, and better meet the needs of travelers and commuters, making transportation systems safer, more efficient, and effective. The goal of this task is to develop algorithms that can automatically detect and localize vehicles in images or videos by training Deep Learning models on labeled datasets of vehicle examples. Object detection using Deep Learning is a related task that involves identifying and localizing objects in images or videos. This task aims to automatically detect and classify objects within a scene and determine their precise location. Object detection using Deep Learning is beneficial in real-time applications such as surveillance systems, robotics, and self-driving cars, and can result in improved safety, efficiency, and automation across various domains.