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LogoutObject detection is a computer vision technique for locating instances of objects in images or videos. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results. When humans look at images or videos, we can recognize and locate objects of interest within a matter of moments. The goal of object detection is to replicate this intelligence using a computer.
Object Detection Using Deep Learning:
We can use a variety of techniques to perform object detection. Popular deep learning–based approaches using (CNNs), such as R-CNN and YOLO v2. automatically learn to detect objects within images.
We can choose from two key approaches to get started with object detection using deep learning:
- Create and train a custom object detector. To train a custom object detector from scratch, you need to design a network architecture to learn the features of the objects of interest. You also need to compile a very large set of labeled data to train the CNN. The results of a custom object detector can be remarkable. That said, you need to manually set up the layers and weights in the CNN, which requires a lot of time and training data.
- Use a pre-trained object detector. Many object detection workflows using deep learning leverage transfer learning, an approach that enables you to start with a pre-trained network and then fine-tune it for your application. This method can provide faster results because the object detectors have already been trained on thousands, or even millions, of images.