AI Based Object Detection: Art To Learn Pixel in the New World with Computer Vision technology

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You must have seen this video on Hollywood movies and web series. Let’s read something about it.

Object detection is a technology that allows computers to identify objects in digital images or videos. It can detect and locate objects such as people, buildings, or cars in real time, and it is used in a variety of applications such as self-driving cars, and security systems. It helps computers understand what is happening in an image or video and identify different objects and their location within the frame.

Object Detection is a combination of image segmentation, feature extraction, and machine learning algorithms. 

Steps Of Object Detection:

1. Image Pre-processing: The first step is to pre-process the image or video, which may involve resizing, cropping, or normalizing the data.
2. Feature Extraction: In this step, features are extracted from the pre-processed image or video. These features are then used to train machine learning algorithms and identify objects. Standard feature extraction techniques include Scale Invariant Feature Transform (SIFT), Speeded Robust Features (SURF), and Histograms of Oriented Gradients (HOG).
3. Object Detection: Apply object detection algorithms to identify and locate objects within the image or video. There are two main categories of algorithms: traditional computer vision-based methods and deep learning-based methods.

Approach Of Object Detection :

Traditional Computer vision-based methods (Early 2000s to 2014).

Traditional computer vision-based methods extract features from images and then use machine learning algorithms to identify objects. The Viola-Jones Algorithm and the FAST corner detection algorithm are examples of traditional computer vision-based methods. These methods were effective in their time(Early 2000s to 2014), traditional computer vision-based methods have been replaced by deep learning methods, which have superior performance and accuracy in computer vision tasks.

Deep learning-based detection(After 2014)

Deep learning-based detection refers to the recent advancements in computer vision using deep neural networks.

Deep learning-based methods, on the other hand, use Convolutional Neural Networks (CNNs) to directly predict the bounding boxes and class probabilities of objects in images. 

Examples of deep learning-based methods include Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO), and Faster R-CNN.

YOLO (You Only Look Once) is a popular deep learning-based object detection method. Over the years, there have been several enhancements to the original YOLO algorithm that have improved its accuracy and speed.

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Finally, the results of the object detection algorithm are post-processed to refine the object detection results, such as removing overlapping bounding boxes or filtering out false positive detections.

Conclusion

In conclusion, object detection is an essential field of computer vision that has numerous applications and is continuously evolving. With the rapid advances in deep learning, object detection has become more accurate and efficient, and is set to significantly impact a wide range of industries in the coming years.