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REAL-TIME OBJECT DETECTION MODEL USING YOLOv10
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From security systems to driverless cars, object detection is essential to many applications. The main goal of this project is to use YOLOv10 and RCNN (Region-Convolutional Neural Network) to perform YOLO (You Only Look Once) object identification in a Flask web application. With notable speed and accuracy gains over its predecessors, YOLOv10 is a state-of-the-art iteration of the YOLO model intended for quick and precise real-time object recognition. Furthermore, by combining region suggestions with CNN for feature extraction, the study integrates RCNN for more accurate object localization. Users can contribute photos or video streams for object detection using these models, which are incorporated into a web application built with Flask. After processing these inputs and performing detection, the application shows the findings along with bounding boxes and recognized objects. Making use of RCNN's and YOLOv10's advantages, the suggested system makes sure that real-time performance and detection accuracy are balanced. The result is a reliable, effective, and user-friendly solution for object detection in practical situations.
Title: REAL-TIME OBJECT DETECTION MODEL USING YOLOv10
Description:
From security systems to driverless cars, object detection is essential to many applications.
The main goal of this project is to use YOLOv10 and RCNN (Region-Convolutional Neural Network) to perform YOLO (You Only Look Once) object identification in a Flask web application.
With notable speed and accuracy gains over its predecessors, YOLOv10 is a state-of-the-art iteration of the YOLO model intended for quick and precise real-time object recognition.
Furthermore, by combining region suggestions with CNN for feature extraction, the study integrates RCNN for more accurate object localization.
Users can contribute photos or video streams for object detection using these models, which are incorporated into a web application built with Flask.
After processing these inputs and performing detection, the application shows the findings along with bounding boxes and recognized objects.
Making use of RCNN's and YOLOv10's advantages, the suggested system makes sure that real-time performance and detection accuracy are balanced.
The result is a reliable, effective, and user-friendly solution for object detection in practical situations.
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