YOLO Object Detection with Opencv and Python

Abstract

This project outlines a Python-based object detection pipeline that integrates the YOLO (You Only Look Once) deep learning architecture with OpenCV's Deep Neural Network (DNN) module. The system is engineered to analyze static images, precisely locating objects by employing a pre-trained YOLO model, which is configured via user-supplied files for its setup, weights, and class labels. Input images are transformed through blob processing to ensure compatibility with the network, then fed into the neural network to acquire predictions from its designated output layers. Object detections are subsequently refined by applying a confidence threshold, and Non-Maximum Suppression (NMS) is utilized to eliminate redundant findings and boost accuracy. Every confirmed object is visually marked on the original image with a colored bounding box and its corresponding class label. The final annotated image is then both displayed to the user and saved for future reference. This codebase provides a versatile and adaptable utility for deploying pre-trained YOLO models on image datasets, establishing a robust foundation for future real-time detection systems or broader computer vision applications.