Project Title:Object Detection with YOLO (You Only Look Once)

Objective:

To implement and train a model for real-time object detection using the YOLO (You Only Look Once) algorithm, which can detect multiple objects in an image or video frame and localize them with bounding boxes.

Summary:

The Object Detection with YOLO project focuses on training a machine learning model to detect and classify objects in images or video using the YOLO algorithm. YOLO is a state-of-the-art, real-time object detection system that can detect multiple objects in an image by dividing the image into grids and predicting bounding boxes and class probabilities for each grid cell.

Unlike traditional object detection methods, YOLO performs object detection in a single pass, making it extremely fast and suitable for real-time applications. The project involves training the model on a dataset (like COCO, Pascal VOC, or custom datasets), and then testing it to identify objects in unseen images or videos.

Key Steps:

Collect Data – Use publicly available datasets like COCO or Pascal VOC, or create a custom dataset with labeled images.

Data Preprocessing – Annotate the dataset with bounding boxes and labels for each object.

Model Implementation – Use the YOLO architecture, which includes a CNN-based backbone (like Darknet) to predict bounding boxes and object classes.

Train Model – Train the YOLO model on the dataset to learn the mapping between images and object locations.

Evaluate Model – Test the model's performance using metrics like Mean Average Precision (mAP) and Intersection over Union (IoU), and visualize detected objects in test images.

Technologies Used:

Python

OpenCV (for image/video manipulation)

TensorFlow / Keras / PyTorch (for deep learning model implementation)

YOLO framework (or pre-trained YOLO models)

Darknet (optional, for YOLO model training)

Matplotlib / Seaborn (for visualizing results)

Applications:

Autonomous vehicles for detecting obstacles, pedestrians, and other vehicles.

Security and surveillance for detecting suspicious activity or individuals in video feeds.

Robotics for object manipulation and navigation.

Retail and inventory management for tracking products and items on shelves.

Augmented Reality (AR) for overlaying information on detected objects.

Expected Outcomes:

A trained YOLO model that can detect multiple objects in images or video with high accuracy.

Visual output showing bounding boxes around detected objects with their class labels.

Evaluation of the model’s performance using metrics like precision, recall, and mAP.