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What is Object Detection?
The definition of object detection is a computer vision technique that can identify and locate objects in images and videos.
It involves a series of techniques that attempt to give machines the ability to emulate human vision, so that they can perceive visual input to identify and classify objects. It is worth noting that there are many different types of object detection algorithms that researchers can use.
Key Takeaways
- Object detection is a computer vision technique that can locate objects in images and videos.
- This technique can be used to identify and classify objects in images.
- Researchers can develop object detection by building a neural network trained on real world images and videos.
- It can be used to give robots and autonomous vehicles the ability to perceive their environments.
- Object detection is a distinct concept from object classification and object segmentation.
How Object Detection Works
Object detection involves using machine learning (ML) algorithms and deep learning algorithms to teach computer systems how to locate objects in images and videos.
A researcher will typically do this by building a neural network, training the network on a vast data set of input images depicting objects from different backgrounds and perspectives, and giving the objects labels so that the model can better distinguish between them.
When analyzing an image, the model will process its underlying grid structure and use pattern recognition to segment images into different regions, corresponding with objects. This involves assessing the size, shape and color of various sections.
The model will also provide the user with a confidence score, a numeric value determining how confident the machine is that the given classification is correct.
It is important to note that while object detection shares similarities with image classification or image recognition, it is a unique concept. Briefly, image classification classifies images according to particular categories, whereas object detection locates and categorizes objects in an image.
What is Object Detection Used For?
In a nutshell, object detection is used to automatically identify and locate objects in images. A user or researcher can input an image and the model will be able to tell you what objects are in the image, and where they are located, at a high speed.
Such solutions are used in It can also be used in autonomous vehicles and self-driving cars, where they can locate other objects on the road, such as other vehicles, pedestrians, road markings, and traffic signs, and to navigate the road without causing a collision.
Object Detection Architecture
Below is a basic depiction of an object detection architecture.
Machine Learning vs. Deep Learning
Machine learning and deep learning can each be used to enable object detection capabilities for computers. While both are similar, they are both different techniques that can be used to train artificial intelligence (AI) systems.
Machine learning algorithms are trained with minimal human assistance to be able to process large data sets and extract insights without explicit programming.
In contrast, deep learning algorithms have the ability to learn from a data set continuously and independently. Whereas a ML algorithm would need human intervention to learn from its errors, a deep learning algorithm can do so independently.
These algorithms also require much higher volumes of data for training.
Many researchers choose to use deep learning algorithms due to their automated capabilities and the increased accuracy of detection.
Object Detection vs. Classification & Segmentation
| Object Detection | Object Classification | Object Segmentation | |
| Definition | Object detection uses machine learning and deep learning techniques to give a computer system the ability to locate objects in images and videos. | Object classification involves grouping objects in images and videos into certain classes. | Object segmentation is where a system splits up a digital image into different groups of pixels to detect objects. |
| Purpose | Can tell you if an image or video contains an object plus where it is located. | Can assign a label to a single image or video. | Can tell you if an image or video contains an object, and can locate it more precisely at the pixel level. |
Object Detection Use Cases
There are a wide range of use cases for object detection.
Some of the most common are listed below:
Object Detection Pros and Cons
There are a number of pros and cons to be aware of when using object detection.
- Detects objects and their locations in real-time
- Minimal need for human intervention
- Given a confidence score with outputs to help with reliability
- Accuracy depends on the variety and quality of the training data
- Objects shown from different angles may go undetected
- Can struggle to detect objects underwater
The Bottom Line
Now you know the definition of object detection, it’s important to note that it offers an automated and scalable way to classify and detect objects in images without the need for human oversight.
In the future, the use of technologies like object detection will play a pivotal role in areas like robotics and autonomous vehicles where machines will need to be able to better perceive their environments.
It will play a vital role in the future of autonomous vehicles and other key areas.
