YOLOV3: An advanced solution for real-time object counting

Abstract

With the evolution of modernity and the new challenges it brings, an advanced solution is presented using the YOLOv3 algorithm for real-time object counting. This consists of the precise detection and counting of objects in images and videos, which leads to diverse applications, such as security, logistics, agriculture, and quality control. The YOLOv3 algorithm, an object recognition system based on machine learning, has proven to be an efficient tool in the process of detecting multiple classes of objects with a high processing speed in a single image or frames in videos. In this study, the YOLOv3 algorithm is used to address the problem of object counting, taking advantage of its ability to detect and locate objects in an image and provide an accurate real-time count. Describing the YOLOv3 configuration process, the model training process, and the evaluation of its performance on different datasets. The experimental results show that the proposed solution offers promising accuracy to object detection and counting, overcoming the limitations of traditional approaches. This study contributes to the advancement of computer vision and provides an effective tool for applications that require real-time object counting.