Detecting Tags for Real-Time Objects in Images Using the Yolo-V3 Algorithm

Real-time object recognition can be a vast, erratic, and complex field of computer vision. The goal is to develop an object detection and counting system using deep learning and image processing algorithms. The whole thing is to develop a software system that requires a single video or image stream. It includes the following parts: The background has no moving objects, and the stage has moving objects. This system determines the difference between moving objects and the number of elements transmitted within the video scene. The experiment was conducted to obtain the following characteristics: Easy to use, to demonstrate that the system can verify object detection and count under specific conditions. Effective, to show that the system operates with high accuracy.