In Machine Learning, the detection of objects aims to detect all instances of objects of a known class, such as pedestrians, cars, or faces in an image. In this article, I’ll walk you through what is object detection in Machine Learning.
What is Object Detection?
Object detection involves the detection of instances of objects of a particular class in an image. Only a small number of instances of objects are present in an image, but there are a very large number of possible locations and scales at which they can occur and which needs to be explored more in detail.
Each step in detection is reported with some form of information. This can be as simple as to detect the location of the object, the scale of the object, or the extent of the object defined in terms of a bounding box. In other situations, the information is more detailed and contains the parameters of a linear or nonlinear transformation.
For example, a face detector which is an object detection application, it can calculate the locations of eyes, nose and mouth, in addition to the bounding area of the face.
Object detection systems build a model for an object class from a set of training examples. In the case of rigid objects, only one example may be necessary, but more generally several training examples are necessary to grasp certain aspects of the variability of the classes.
Categories of Object Detection Models
The methods of detecting objects from an image fall into two broad categories; Generative and Discriminative. Generative consists of a probability model for the variability of objects with an appearance model. The parameters of the model can be estimated from the training dataset and the decisions are based on later odds ratios.
Discriminative generally construct a classifier that can classify between images containing the object and those not containing the object. Classifier parameters are selected to minimize errors in training data, often with a regularization bias to avoid overfitting.
The two categories of objects detection, the generative and discriminative models, begin with an initial choice of the characteristics of the image and with a choice of the latent pose parameters which will be explicitly modelled. The main differences between generative and discriminating models lie in the learning and computational methods.
A major distinction is that generative models do not need background data to train the object detection model, while discriminative methods need data from both classes to learn decision limits.
Applications of Object Detection
Objects detection has a wide range of applications in a variety of fields, including robotics, medical image analysis, surveillance, and human-computer interaction.
Face detection is a typical application of object detection systems. There has been significant success in deploying face detection methods in practical situations such as current digital cameras use face detection to decide where to focus and even detect smiles to decide when to shoot.
Here are some of the machine learning projects based on the object detection task:
- Car Detection
- Face Mask Detection
- Age and Gender Detection
- Image Segmentation
- Google Landmark Detection Model
Hope you liked this article on what is object detection. Please feel free to ask your valuable questions in the comments section below.