Detecting mobile objects with ai using edge detection and background subtraction techniques

Abstract

This study explores the mathematical foundations integral to the training process of YOLO (You Only Look Once), a prominent object detection algorithm in computer vision. Key mathematical concepts, including bounding box representation, Intersection over Union (IoU) calculations, Mean Squared Error (MSE) for objectness prediction, Non-Maximum Suppression (NMS) for post-processing, and learning rate scheduling, are elucidated. This article shows the use of a number of methods for obtaining effective results of moving objects in the YOLO library working in the Python programming language. It is provided with the optimal options of the program codes for the optimal results. Exploring anchor boxes, backpropagation, and data augmentation reveals their crucial role in refining YOLO's accuracy and generalization. This evolution showcases YOLO's transition from basic frame discrimination to advanced models adept at dynamic scene handling. Emphasizing practical implications, it underscores YOLO's effectiveness in real-time object detection across diverse applications.