object DETECTION BASED ON ARTIFICIAL INTELLIGENCE

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1. Object Detection

2. Object detection is a computer vision technique that involves identifying and locating objects within an image or video. The goal is to not only classify what objects are present in the scene but also to locate their positions using bounding boxes. Overview Object Detection

3. The image is processed through a convolutional neural network (CNN) or similar architecture to extract relevant features. These features are representations of patterns in the image that help in distinguishing different objects. Feature Extraction

4. For each of these regions, the algorithm performs classification to determine what object (or objects) are present. This is usually done using another part of the neural network that is trained to classify objects based on the extracted features. Bounding Box Prediction :

5. The output of an object detection algorithm typically includes: Output The class labels of detected objects (e.g., "car", "person", "dog"). The coordinates of the bounding boxes around each detected object. Confidence scores that indicate how certain the algorithm is about each detection.

6. Object detection is used in a wide range of applications, such as autonomous vehicles, surveillance systems, medical imaging, and image retrieval systems. Popular object detection architectures include Faster R-CNN, YOLO (You Only Look Once), and SSD (Single Shot Multibox Detector), each with its strengths and trade-offs in terms of speed and accuracy. Applications

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