Comparative Analysis of Drone Detection Model Using YOLO Versions

This article discusses a structured way for categorizing drones that combines deep learning with a number of other methodologies. It is impressive that this method was able to identify both fixed and moving objects with great accuracy. The You Only Look Once (YOLO) object detector is a well-liked and efficient method for object recognition. Convolutional Neural Networks (CNNs) have been developed for the analysis of visual data, automatically learning hierarchical representations of features from raw pixel inputs. The model is trained using the required dataset for 120 iterations in order to identify drones. In general, deep learning methods have transformed the field of computer vision, enabling impressive improvements in a range of tasks like picture classification, object recognition (including drone detection), segmentation, and image synthesis. To determine the optimum model for drone identification, different YOLOv7 models (YOLOv7, YOLOv7-X, YOLOv7-W6, YOLO-E6, YOLOv7-D6, and YOLO-E6E.) are compared.