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Object Detection Using YOLOv3 and OpencV

• Source code

Prerequisites

• Foundation knowledge of ComputerVision
  • OpenCV (Open Source Computer Vision Library) is not the original implementation of YOLOv3, but it can be used to work with YOLOv3 and perform object detection using pre-trained YOLOv3 models.
• Knowledge on YOLOv3 Deep learning object detection algorithm
• Python Language

Description

This project is an implementation of real-time object detection using the YOLOv3 (You Only Look Once version 3) algorithm combined with the power of OpenCV (Open Source Computer Vision Library). The aim of the project is to detect and localize multiple objects in images and video streams, providing bounding boxes around the identified objects with corresponding class labels and confidence scores.

Key Features

• Utilizes the YOLOv3 pre-trained model for object detection.
• Employs OpenCV for image and video processing, making it efficient and suitable for real-time applications.
• Supports the detection of a wide range of objects, including people, vehicles, animals, and various everyday objects.
• Provides accurate bounding box predictions along with class labels and confidence scores for each detected object.

Usage

1. Install the required dependencies and libraries.
2. Run the main script to load the YOLOv3 model and perform object detection on a single image or video stream.
3. View the output with annotated bounding boxes and class labels.
4. Customize parameters and experiment with different images or videos.

Dependencies

• Python 3.x
• OpenCV
• YOLOv3 pre-trained weights and configuration files

Before running the project, make sure to download the necessary YOLOv3 pre-trained files and update the file paths accordingly.