This project implements a real-time image and video object detection classifier using pretrained yolov3 models.

Diving into Object Detection and Localization with YOLOv3 and its architecture, also implementing it using PyTorch and OpenCV from scratch.

deep learning based object detection using YOLOv3 with OpenCV

This AIM of this repository is to create real time / video application using Deep Learning based Object Detection using YOLOv3 with OpenCV YOLO trained on the COCO datasets. The COCO dataset consists of 80 labels.

Fish object detection with YOLOv3 on opencv-python.

Social distancing detection, a deep learning computer vision project with yolo object detection using python, OpenCV and YOLOv3 model.

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|| Using OpenCV and cv_bridge with Nodelets and YOLOv3 for Object Detection ||

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An implementation of real-time object detection with a web camera using YOLOv3 and OpenCV.

Implementation of YOLOv3 with OpenCV for color based classification of objects. (Demonstrated for football team detection)

This project implements object detection using YOLOv3 with pre-trained weights. It supports live detection from a webcam, image detection, and video detection. The application is built using Python with libraries such as OpenCV, PIL, and Tkinter for the GUI, and runs primarily through a Jupyter Notebook interface.

Real-time object detection system using ESP32-CAM and YOLOv3 with Python OpenCV

Real-time object detection system using YOLOv3-Tiny and OpenCV with live camera feed.

An implementation of Object Detection using YOLOV3, SSD MobileNet and OpenCV in Ubuntu for Drones provided with an RTSP Video streaming url

A tool for counting the number of vehicles in a video, utilizing OpenCV and YoloV3 for object detection, with the vehicle type count displayed on the video.

Object Detection project leverages the YOLOv3 model with OpenCV to detect and label objects in images, applying non-maximum suppression for accurate results. It involves loading the model, preprocessing input images, and visualizing detected objects with bounding boxes and labels.

YOLO (You Only Look Once) is a very powerful and a fast algorithm in object detection. A strong understanding of the algorithm is essential before we start to code. Some important papers to start with - There are three papers you need to go through (Maybe difficult to understand initially, but worth reading it) You Only Look Once: Unified, Real-Time Object Detection YOLO9000: Better, Faster, Stronger YOLOv3: An Incremental Improvement We are going to use YOLO v3 for our coding purpose in this repository. Before going to code, we need to download some important YOLO files. It's the folder that's present in this repository as yolo-coco The three files that needs to be downloaded are - coco.names yolov3.cfg yolov3.weights Download these files and save it inside a folder. Name the folder anything you wish, but I have named it as yolo-coco just because of the fact that we are going to use the coco dataset objects. Create a folder images and have some pictures inside it to test the object detection. The yolo.py has the script to detect the objects in the images. Make sure you have numpy and opencv installed. If not install them using pip pip install numpy pip install opencv-python I am using the numpy version 1.17.4 and opencv version 3.4.2 You can now run the file by giving this command on your command promt python yolo.py --image images/ipl.jpeg You can use any image you want after the --image argument. Make sure you give the right path. Press q to quit the window of the image showing object detection

Object Detection Algorithm with YOLOv3 and OpenCV

Object detection with YOLOv3 (OpenCV DNN) - COCO 2017

Custom Object Detection and Data Scraping with Yolov3, Flask, OpenCV, and Javascript

🎯 Real-time object detection with YOLOv3 and OpenCV. A desktop app with a Tkinter GUI for live detection of over 80 objects from your webcam.

VisionNote is a simple object detection module leveraging the power of YOLOv3. Developed with the support of OpenCV.

This project implements real-time object detection using the powerful YOLOv3 (You Only Look Once) algorithm with OpenCV in Python.

This project demonstrates real-time object detection using the YOLOv3 (You Only Look Once) deep learning model and OpenCV with webcam input.

This project is a real-time object detection system built with **Flask**, **OpenCV**, and **YOLOv3**. The application streams video from your webcam, detects objects using the YOLOv3 model, and displays the detections in the browser. It also provides a dynamic slider to adjust the confidence threshold for object detection.

This project is a real-time object detection system built with **Flask**, **OpenCV**, and **YOLOv3**. The application streams video from your webcam, detects objects using the YOLOv3 model, and displays the detections in the browser. It also provides a dynamic slider to adjust the confidence threshold for object detection.

Real-Time Object Detection using YOLOv3 and OpenCV. This project leverages a pre-trained YOLOv3 model to detect and classify objects in real time using your webcam. Bounding boxes and labels are drawn around detected objects, with dynamic object counting displayed on the screen.

Real-time object detection using YOLOv3 and OpenCV. Detects 80+ everyday objects like people, cars, animals, etc. via webcam. Simple Python implementation for beginners to learn computer vision and AI concepts with live video detection.

Real-time self-driving simulation in GTA V using Python and OpenCV, implemented lane detection (Hough Transform) and multi-object detection (cars, pedestrians, traffic lights) with YOLOv3 and proximity warnings.