Object detection is the foundation for many computer vision techniques. At a high-level, object detection is exactly what it sounds like: the process of finding or “detecting” objects in images/videos. Though there are many different varieties of object detection, the bulk of these techniques rely on a machine learning algorithm to train a model to recognize desired objects in an image/video. A model is essentially a specialized program that is fed a data set (in our case, a very large number of images) in an effort to find patterns to be used to make an informed decision.
For example, say you wanted to train a model to recognize a dog in images and videos. In order for the model to produce accurate results, you’d want to feed it “positive” results (i.e. images containing dogs) and “negative” results (images that don’t contain dogs). Over time, the model will begin to recognize patterns in the images containing the desired object (dogs) and deliver accurate results when used to detect those objects in an image/video. Granted, this is perhaps an oversimplification of the process, but that is the general idea. The accuracy of the results you get depend heavily on the size of the data set the model has ingested for training (the more, the better). As the data set grows, the model has the potential to become significantly more accurate.
Image Classification/Recognition is the process of analyzing an image in order to extract meaningful information about its contents through machine learning. The goal of image classification is to determine a “category” that accurately describes the contents of an image. This can be used to find specific objects, or to figure out what the image is about.
Whereas object detection is used to detect the presence of a particular object (or objects) in an image, image classification is the process of then assigning that image to one or more categories based on its contents. Two terms often used when discussing image classification are single-label and multi-label. You can think of labels as being synonymous with categories. The goal is to be able to identify mutually exclusive characteristics existing within an image and assigning it to one (or more) categories respectively.