Computer Vision - Object Detection with OpenCV and Python

Introduction

Provide an introduction to the concept of object detection in computer vision. Explain its significance and applications.

Tools and Libraries

OpenCV

Explain what OpenCV (Open Source Computer Vision Library) is and why it's used for object detection.

Python

Discuss why Python is chosen as the programming language for this project.

NumPy

Mention NumPy's role in handling numerical operations and data structures, which is crucial for preprocessing and handling data in object detection tasks.

Implementation Details

Preprocessing

Discuss any preprocessing steps applied to the input data (e.g., resizing, normalization).

Object Detection Script

Walk through the implementation of the object detection script:

Loading the Model: Explain how to load the pre-trained model.

Running Inference: Detail the process of running inference on input images or videos.

Postprocessing: Describe any postprocessing steps applied to the detected objects (e.g., filtering, non-maximum suppression).

Example Code Snippets

Provide code snippets from your implementation to illustrate key points.

Performance Evaluation

Discuss how you evaluated the performance of your object detection model. Include metrics used and results obtained.

Usage Tips

Offer practical tips for using your implementation effectively.

Future Improvements

Discuss potential improvements or extensions to your project (e.g., using different models, optimizing performance).

Conclusion

Summarize the key points discussed and highlight the significance of your project in the context of object detection and computer vision.

• This file are created by @Ankitwarbhe under the guidance of Ilias Papachristos

• A special thanks to @rsrinath14 for your valuable contribution