Object Detection

Why is Object Detection important?

Object Detection plays an important role in aspects of image retrieval systems and video surveillance. Even though Objection Detection is a technology behind advanced driver assistance systems (ADAS), its importance is very much obvious in various sectors of practicality. No matter what you choose to work with between machine learning vs deep learning for the vast field of computer vision, object detection will have its undeniable importance and functions along the way.

How Does Objection Detection Work?

There is no lack of variety of techniques to perform objection detection successfully. Commonly used deep learning approaches using convolutional neural networks (CNNs), such as YOLO v2 and R-CNN, learn to identify objects in visual input by default. There are two main approaches to functional object detection.

1.      Train and create a custom Object Detector

A network architecture must be designed to learn the aspects of the object of interest in order to train a custom object detector right from the very start. Similarly, to train the CNN, a huge amount of labeled data needs to be compiled. Before you get remarkable results, you must manually set up the weights and layers in the CNN and that takes a lot of time and training data.

2.      A pre-trained Object Detector

Transfer learning is leveraged by many object detection for computer vision workflows, it is an approach that allows you to begin with a pre-trained network and fine-tune for your application. Because this method already trains thousands and even millions of images, faster results are provided.

AI Australia works will show you that whether you use a pre-trained object detector or a custom one, it will vary what kind of object detection network you want to use: a single-stage network or a two-stage network. So, computer vision object detection becomes a mandatory goal to achieve when it comes to computer vision roles and tasks.