The technology-related concepts might be a little bit confusing for a layman to understand. With the advancement of time and technology, the knowledge related to machine learning is very important. Image processing and object recognition are some of the concepts that are related to the deep learning of machine learning. the computer-related vision tasks can be a bit difficult for beginners to consider and understand and distinguish.
Many believe that both the terms “object localization” and “object detection” are the same. but in fact, of the deep concepts, they are actually different. Both these facts come from the topic of Image classification from the Deep learning methods of machine learning and computer vision. The image recognition concept is very important nowadays to companies as it helps in the categorization and organization of the database through the automated classification of images in bulk. Object recognition is a part of image recognition that is more deep and useful.
Object recognition, in basic terms, is a series of computer vision tasks that is related to identifying or locating the objects present in the digital images, photographs, and videos. There are two concepts in Object recognition, image detection related to the processing of the image and assigning a class label, and object localization refers to the drawing of a bounding box over one or more objects in an image. Thus, Object recognition or object detection can be referred to as classifying the objects in the image, counting on the objects present in the screen, tracking their location precisely, and accurately labelling them according to their class.
There are various forms of codes and object detection algorithms that are used in this object detection process, like TensorFlow object detection or Yolo object detection. The whole process of Object detection involves the follow-up of the process through an object detection flowchart and
There are different object detection techniques that are used in the process of Object recognition, and that is:
In another form, these forms are also known as the machine learning-based and deep learning-based approaches. Let’s look into them one by one to learn more about them in detail.
R-CNN family of Models or also known as R-CNN, stands for Region-Based Convolutional Neural Network. R-CNN is a technique that is performed for the object recognition or object localization process. A Convolutional Neural Network (CNN) is a multilayered neural organization with an extraordinary design to distinguish complex highlights in the information. CNN’s have been utilized various object detection examples like controlling vision in robots and for self-driving vehicles. This model is built to classify the contents present in various images.
It basically works in a series of tasks performed one by one. the process followed by the CNN can be summed as follows:
You can use the R-CNN Model to do any image classification. All that we have to do is training images and
Another most recognized family of object detection models is YOLO. YOLO stands for You Look Only Once. In this Model, the image is split into grids of cells, and each cell is responsible for forecasting a bounding box if the focal point of a bounding box falls inside the cell. Each cell predicts the box that involves the x,y coordinate and also the height, width, and certainty. After the prediction made by each cell, a total class prediction is made. Although this technique offers a low predictive accuracy or more locational errors, object detection using Yolo is a more stable technique and also designed for real-time use and speed.
The whole object detection procedure involves the applying of the object detection algorithm that can help in the process of detecting various objects present in the image. Sometimes, an object detection sensor is used on object detection images to find the location of the objects as well as the labelling of the objects. There are various object detection applications in real life for purposes, like in the area of Video surveillance, Self-driving cars, or Anomaly detection areas like agriculture industries. As the area of the use is varied and large in real-life, object detection use cases can be seen everywhere, although they might not be noticed.
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