https://pjreddie.com/darknet/yolo/

• We are using the pretrained YOLO model trained on COCO dataset 

• First we load the pre-trained YOLO model using Deep Neural Network library of OpenCV.

• Convert the frame to a blob

• Process the output and draw bounding boxes

• Object detected using the pretrained weights and dnn(Deep neural library) library of openCV. 

• Display the resulting frame.

Proof of Concept Document: Real-time Object Detection using YOLO

Objective:

The goal of this Proof of Concept (POC) is to demonstrate real-time object detection using the YOLO (You Only Look Once) deep learning model. The implementation utilizes the OpenCV library in Python.

Technologies Used:

• OpenCV (cv2): A popular computer vision library that provides tools for image and video analysis.

• Deep Neural Network Libraries of OpenCV.

• NumPy: A library for numerical operations in Python, used for handling arrays and matrices.

Code Overview:

The provided Python script uses the YOLOv3 model to perform real-time object detection on a webcam feed. Here is a brief overview of the key components:

• Loading YOLO Model:

• The YOLO model is loaded using cv2.dnn.readNet, specifying the path to the model weights (yolov3.weights) and configuration file (yolov3.cfg).

• The COCO class names are loaded from the file coco.names.

• Webcam Capture:

• The script captures video frames in real-time using the OpenCV VideoCapture object (webcam with index 0).

• Object Detection:

• Each frame is preprocessed into a blob using cv2.dnn.blobFromImage.

• The blob is fed into the YOLO model using net.setInput(blob), and the output is obtained with net.forward(layer_names).

• The output is processed to identify objects with confidence greater than 0.5.

• Non-maximum suppression (cv2.dnn.NMSBoxes) is applied to filter out overlapping bounding boxes.

• Drawing Bounding Boxes:

• Bounding boxes are drawn around detected objects on the frame.

• The class label, confidence score, and box coordinates are displayed for each detected object.

• Display and Exit:

• The resulting frame with bounding boxes is displayed in a window titled "Real-time Object Detection."

• The script continues to run until the user presses 'q', at which point the video capture is released, and the OpenCV window is closed.

Execution:

To run the script, make sure you have the YOLOv3 weights, configuration file, and COCO class names file in the same directory as the script. Execute the script, and a window will open showing real-time object detection from your webcam feed.

Conclusion:

This Proof of Concept demonstrates a simple yet effective implementation of real-time object detection using the YOLOv3 model and OpenCV. It serves as a foundation for building more advanced computer vision applications and can be extended for various use cases.