Create a very nice detailed lecture on the following mathematically taking a small image, frame or a dataset Classification with Localization Sliding Window Concept Sliding Window via CNN itself Object Detection: ● You Only Look Once (YOLO) ● R-CNN ● Fast R-CNN Landmark Detection

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Object detection is a fundamental computer vision task that goes beyond simple image classification. While classification tells us what objects are present in an image, object detection tells us both what objects are there and precisely where they are located using bounding boxes. The sliding window approach is a traditional method for object detection. We define a fixed-size window and systematically slide it across the entire image. At each position, we extract features and use a classifier to determine if an object is present. This process is repeated at multiple scales to detect objects of different sizes. R-CNN revolutionized object detection by introducing region proposals. Instead of checking every possible location, it first generates about two thousand candidate regions using selective search. Each proposal is then processed through a CNN to extract features, classified using support vector machines, and refined with bounding box regression. YOLO revolutionized object detection with its single-shot approach. Instead of generating proposals, it divides the image into a grid and each grid cell directly predicts bounding boxes and class probabilities. This enables real-time performance by processing the entire image in a single forward pass through the network. To summarize what we have learned: Object detection has evolved dramatically from computationally expensive sliding window approaches to sophisticated deep learning methods. R-CNN introduced the concept of region proposals, while YOLO revolutionized the field with its single-shot approach enabling real-time detection. These advances have made object detection practical for real-world applications.