Object Detection

Introduction to Object Detection Object detection is a crucial task in the field of computer vision, with applications ranging from surveillance systems to autonomous vehicles. One of the most popular and efficient algorithms for real-time object detection is YOLO (You Only Look Once). In this article, we will guide you through the process of creating a real-time object detection system using YOLO and OpenCV. Prerequisites Before diving into the implementation, ensure you have the following prerequisites: ...

Introduction to YOLO and Object Detection Object detection is a fundamental task in computer vision that involves identifying and locating objects within images or video frames. One of the most advanced and efficient algorithms for this task is YOLO (You Only Look Once). YOLO is known for its speed and accuracy, making it suitable for real-time applications such as self-driving cars, surveillance systems, and robotics. How YOLO Works Preprocessing: The input image is resized to a fixed size and the pixel values are normalized. Convolutional Neural Network (CNN): The preprocessed image is passed through a CNN to extract feature maps. Object Detection: The feature maps are fed into detection layers, which predict the class probabilities and bounding box coordinates for each cell in the feature map. Non-maximum Suppression (NMS): The predicted bounding boxes are filtered using NMS to remove overlapping detections. Output: The final output is a set of bounding boxes with class labels and confidence scores. Setting Up the Environment To start working with YOLO and OpenCV, you need to set up your environment with the necessary libraries. ...