Object Detection vs. Image Classification: What Is the Difference?

Image classification and object detection are both important components of computer vision, the technology that allows computers to process visual data and make decisions accordingly. Image classification describes an algorithm that can process images and predict a category for each image, such as labelling an image of a cat as a “cat.” Object detection is a slightly more complicated algorithm that can determine more than one object or instances of objects in an image and understand where the objects are in relation to one another. 

Examine object detection versus image classification in more detail to learn how you can use them together or separately to solve a variety of machine learning problems. If you're ready to build your machine learning knowledge, consider enrolling in the IBM Machine Learning Professional Certificate, where you'll learn how to compare and contrast different machine learning algorithms by creating recommender systems in Python.

What is the difference between object detection and image classification?

Object detection needs image classification to function, but the two concepts differ because object detection goes beyond the capabilities of image classification to understand more about the image. For many applications in computer vision, you may use object detection and image classification together to allow your algorithm to understand your data more complexly. 

Image classification can predict which category an image belongs to, while object detection identifies instances of objects and predicts the categories they belong to individually. Object detection is a more complex algorithm that uses image classification to predict classes, but it can understand what is pictured in the image in a more nuanced way. Both share roots in deep learning, a machine learning subset that empowers digital systems to make sense of complex data and build knowledge from it. Each plays a critical role in automating visual data processing. 

What is object detection in computer vision?

Object detection is the ability for a computer to look at an image or picture and understand and classify the objects contained in that image, and pinpoint their location. These algorithms can understand each entity pictured in an image, classify them individually, and recognize their arrangement. Doing so requires the object detection algorithm to analyze the image’s pixels, determine where the objects are based on how the pixels change, create a box approximating the shape of the object, and classify them accordingly.

For example, a self-driving car looking at an image of the road ahead can tell something is blocking the road because the pixels don’t match the algorithm’s training data, and alert it that the image doesn’t look like the road should appear. The algorithm will create a box around the object and use either supervised or unsupervised learning to predict what class the pixels inside the box belong to. In supervised learning, the algorithm will compare the pixels to predetermined classes it learned in training. In unsupervised learning, the algorithm will use reasoning to predict the class the pixels belong to. 

What is object detection used for?

Object detection offers a broad range of use cases, including facial recognition as part of video surveillance, autonomous vehicles, and more. It empowers computers with the data needed to identify objects in the picture and comprehend the number of instances of each object in the picture, as well as each object’s precise location within the image. This information can give the algorithm insight about the next moves it should take, such as steering to avoid a pedestrian, alerting the authorities about an intruder, or rejecting an item during the manufacturing product inspection. Three significant uses include the following: 

• Visual inspection: Object detection in computer vision makes it possible to automate visual inspection in manufacturing. Using object detection, machine learning algorithms can “look at” items as they come off the production line and reject items that show signs of defect. 

• Robotics: Object detection allows robots to move around in their environment, similar to how autonomous cars do: by observing their environment and detecting and reacting to objects they see. For example, a robotic vacuum cleaner can create a “map” of the perimeter of its boundaries to ensure it’s passed along every part of the floor. 

• Video surveillance: Object detection can also power smart surveillance systems that can identify events like packages being delivered and strangers lurking around your house. These systems use motion detection to capture events and filter them using object detection, so you won’t get alerts for the wind rustling the leaves, but you will get an alert when your package arrives. 

What is image classification?

Image classification is a computer vision process in which the machine learning algorithm determines whether images belong in one class or another. Image classification is part of object detection, but it only assigns one class to each image, regardless of whether the image contains multiple instances of the object class or more than one object. 

Machine learning image classification algorithms work by processing the individual pixels of each image and predicting its class, either by comparing them to preassigned classes (in the case of supervised learning) or by recognizing patterns within the images and predicting what classes would make the most sense (in the case of unsupervised learning). 

Image classification applications

Image classification is a foundation within computer vision. In a way, you could say that everything you can use object detection for, you can also use image classification for, because it provides the foundation for computer vision and more advanced tasks like object detection. However, you may also encounter scenarios in which object detection isn’t necessary, and image classification by itself can accomplish the work. Explore three examples from health care, construction, and satellite imagery. 

• Health care: Image classification can help radiologists understand and interpret medical images. Classification algorithms have shown promising results in quickly and accurately classifying medical images. This is important as the increasing workload of radiologists makes it challenging for the number of professionals in the field to manually classify the number of images they need to. 

• Construction: Image classification in construction could allow project managers to better oversee their work sites by using computer vision to help manage their work, such as evaluating the materials currently present on the job site (including hazards). Project managers could use this technology to better manage projects even in remote areas without high-speed internet, a common problem for construction sites where the infrastructure for high-speed internet has yet to be established. 

• Satellite images: You can use image classification to analyze the features present in satellite imagery or maps. By providing labels like land for agriculture, commercial land, or forest, an image classification algorithm can treat small pieces of the map as individual images and assign them a class from the labels you provided. You can use this type of computer vision to visualize maps better and analyze the data. 

What is the difference between image retrieval and image classification?

Image classification and image retrieval are similar ideas, but are separate tasks. Image classification is the process of assigning a class to an image, while image retrieval is locating an image of an object or similar to an object. When you search for images on a search engine, the algorithm uses image retrieval to find images that contain your search words. 

Object detection vs. image classification

While object detection and image classification are both fundamental components of computer vision, they also each present unique strengths and weaknesses as machine learning techniques. Overcoming them may require using them together or in connection with other ML techniques. 

For example, image detection can quickly sort images into categories, but it can only assign one class to each label, limiting an image detection algorithm’s ability to process images with more than one object accurately or that need to go into more than one category. Object detection can accomplish this, but it can have a difficult time identifying very small objects or objects in a group. 

A third kind of computer vision task, image segmentation, takes a more exact approach than object detection. It examines the individual pixels of the image to determine where objects are and where they stop and start. 

Many computer vision algorithm architectures use both image classification and object detection. For example, a region-based convolutional neural network (R-CNN) is a type of algorithm that segments an image into regions or boxes and then methodically works through those boxes to understand what is inside each before using that information to understand the image as a whole. This type of algorithm is a two-step detection algorithm because it first processes the images into areas and then classifies each area’s image. 

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