What is the difference between object detection and object classification?
These two tasks are popular applications of convolutional neural networks. However, I don't understand the difference. According to one Caffe manual, it seems that the task of detecting objects is more difficult.
Does object definition determine where the object is in the image, or does it show how many objects are in the image ...?
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The difference is as follows. If you need to determine the class of the image, it belongs to the task of classifying objects. If you need to determine the coordinates of an object in an image, then this is an object detection task.
To learn more about practical applications of convolutional neural networks for object detection, you can read this article: http://rnd.azoft.com/convolutional-neural-networks-object-detection/
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Discovery is a process of identification, and classification is the categorization of an object based on previously defined classes or types. Although both are based on distinguishable properties of an object, classification can take arbitrary boundaries based on the problem area and is independent of detection.
it seems that the task of detecting an object is more difficult.
How much more difficult than others depends on the specific properties being studied, error fields, accuracy, etc. For example, if there is a tighter detection tolerance than classification, then it can be perceived as more difficult. But in an iterative application that alternates between discovery and classification, which is harder, perhaps not so easy to say.
Does object definition determine where the object is in the image, or does it show how many objects are in the image ...?
Technically, the detection should be unambiguous, perhaps a boolean T or F. All other properties such as location, how many and all other properties are included in the classification. This is not to say that these properties are not related to detection, but after detection, the task now becomes one of the classifications. Where this exact line or transformation occurs depends on the specific application.
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Classification of objects . You are presented with an image, categorize this image into some class like Apple, bus, forest, etc.
Object detection . You are given an image, find out if there is a patch (or coordinates) where the class exists? eg. - Given the image, whether the classes (e.g. oranges, truck, lion) predict on the image or not.
Typically, problems are associated with these methods. First of all, you need to do object detection in the given image and remove the required patch. Then apply the classification to predict its grade (Both can be grouped in the same step)
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Object detection answers the question "Has the object been found?" (Well no). Anything that is not binary is then classification or recognition (for example, face recognition, not face classification). With images, the term “recognition” is often better suited because there is always some ambiguity, and “recognition” reflects those specific problems that are found when objects are detected in images.
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