What is Object Detection in Computer Vision?

Have you ever wondered how Google Lens is able to identify and compare various images that we click with the humongous database of images that Google Images has? Have you ever wondered how medical systems are able to detect broken bones from X-Ray scans? Have you ever wondered how Instagram and Snapchat filters are able to detect your face and apply beautiful filters on the same? Object detection in computer vision.

Computer Vision is a field that is advancing quickly, and research is being conducted to make computer vision models more robust and accurate. One of the most fundamental problems of computer vision is object detection which forms the basis of many complex problems. To make the readers understand in the most basic words, object detection is the phenomenon where a certain class or category of objects is being detected in a scene or in an image. Let’s say there is an image of pet animals. Then different categories, such as dogs, cats, birds, etc would be the objects that we would be looking for to detect in the same image. Object detection not only focuses on the existence of the objects in a digital piece of information but also focuses on the spatial information pertaining to the existence of these objects.

Knowing object detection is the foundational step one should take if one aims to become a good computer vision engineer. This is because object detection is foundational for complex problems like tracking objects, captioning images, and segmenting images as well. There are unlimited use cases of object detection, the simplest of which have been mentioned in the beginning and involve – element classification in a scene, license plate detection, medical science, and even augmented reality.

Understanding Object Detection In-Depth

While treading on the journey to become a computer vision engineer, one popular problem that arises is that there needs to be specific hardware to run the algorithms. While this seems like a good enough reason not to continue with this field and choose something that comes under your budget, engineers around the world, with their knack for problem-solving, have fixed this as well. Support for computer vision projects is now available in the cloud with a pay-per-use amount of RAM and GPU, which makes state-of-the-art hardware available right inside your browser. Not only this but APIs ready to be deployed are also made available, which can directly be incorporated into your projects, and you are provided with pre-trained models that are robust as well as accurate.

You just need a machine, an internet connection, and an account with your cloud service provider, and you’re good to go. Even a low-end machine can now create models that perform well enough as models on high-end machines do. There are no resource constraints that one has to go through anymore.

One notable thing that has to be kept in mind while working with object detection problems is that object detection, and human detection are two separate things. One should not be confused with another. Object detection is finding its use today in manufacturing as well. Pipeline tracking, analyzing the behavior of various robotic machines, and even defect inspection on a microscopic level can be performed using object detection. Object detection is also being used for security purposes to detect intruders as well as detect any disastrous phenomenon such as leaks, fires, etc. The amalgamation of artificial intelligence with computer vision and object detection has made it possible for such advanced models to come to life. It can successfully be said that object detection is no longer just the study of categorization of the existence of different instances in a digital scene.

Digital identities have also become very popular with the increase in usage of features such as Face ID in iOS devices. However, as there was a significant difference between object detection and object recognition, there is also a difference between facial detection and facial recognition. Facial recognition occurs when a person is supposed to be authorized or authenticated or identified based upon the existing details in a database, whereas facial detection is when a person has to be detected irrespective of the traits they possess. In other words, our facial features differentiate us from other human beings. When facial recognition is happening, these traits play the most important role and are elementary in helping us understand “who” is accessing the information, while facial detection does not require the understanding of these details and only focuses on finding if there is “any” face in the digital scene by utilizing features which it has been pre-trained on that can be eyes, nose, ears, lips, etc.

Characteristics of a Well-Performing Object Detection Algorithm

Now that we have understood what object detection is and the possibilities it holds in real life, before building our own object detection model, it is important to also understand the characteristics or parameters on which the model performance will be evaluated. Let us look at each one of them one by one:

1. Accuracy: The most fundamental and obvious parameter will be if our model is giving us accurate answers. The accuracy of object detection models can be defined as when the model is able to detect the correct categories of the objects present in the digital scene.
2. Inference Time: Inference time is the time that the model takes to identify the correct category of the objects present in the digital scene. The shorter the inference time, the better the performance of the deep-learning object detection model.

Real-Life Applications of Object Detection

Object detection has various real-life applications. Dealing with images, videos, and real-time digital scenes creates an unlimited amount of possibilities to involve technology in making lives better. Given below are some broad use cases that one will come across while utilizing object detection.

1. Retail Stores: Retail stores witness the participation of thousands of customers every day who are coming into their stores to buy items for their homes, workplaces, and personal items as well. Object detection can be used to keep a check on the number of people coming in, recognize the items being sold and get a bill made out of the same, thereby automating the billing process. It can also identify any blockers in any store aisles, and can also identify any shoplifters and thieves. Object detection and artificial intelligence combined can be used to decrease customer waiting time and increase profits.
2. Self-Driving Vehicles: Self-driving vehicles detect the obstacles around them to prevent accidents and damage. They also detect the signals and the road signs to act accordingly. Object detection and artificial intelligence are used together in these autonomous vehicles to get a better understanding of the world around the vehicle and then ensure that the vehicle and the passengers inside are being transported safely.
3. Security: Object detection and people detection specifically can be used in surveillance systems and also help to identify wanted criminals and terrorists in real-time from CCTV footage.
4. Roadways: Traffic control and challan calculation can be easily done with object detection and artificial intelligence combined. One can also use the same technique to identify suspicious vehicles in locations to mitigate potential bomb threats.
5. Medical Science: Assessing internal organs has only been made possible with MRI and CT scan technology. While everyone has been relying on the naked eye to assess any issues that might arise in these scans, which can be fatal or need treatment, having machines to automate the process becomes necessary for the early detection of such diseases and to prevent any human errors.

Object Detection Algorithms

Given below are the most popular object detection algorithms. Readers are encouraged to draw a contrast between the algorithm they are currently reading and the previous algorithms that they have read in order to understand the pros and cons of the same.

1. YOLO (You Only Look Once): As the name suggests, the YOLO algorithm only takes a glance at the image once, and then whatever features it is able to extract from the image are used in the final output. It is the most basic kind of object detection algorithm that one comes across in computer vision.
2. R-CNN (Region-Based Convolutional Neural Network): Region-based convolutional neural networks focus on areas of the image and draw a bounding box around the focus part in a specific subregion and label them according to the categories available using the power of convolutional neural networks.

Summary

In this article, we understood in depth what it means to work with object detection and the various use cases this technique has in real life. For a computer vision engineer, knowing and learning about object detection in depth becomes a mandate as this is a problem that becomes the basis for several complex algorithms as well. Object detection can be used for still images, motion pictures as well as real-time data as well depending upon the use case as well as the artificial intelligence techniques that are being deployed with it. Several companies, such as Meta, Tesla, and Snapchat, are investing their time and resources into coming up with better deep learning, computer vision, and object detection algorithms to enhance their products and provide users with a better user experience.

Readers are encouraged to try and implement the algorithms which are mentioned towards the end of the algorithm and understand the difference in accuracy and inference time that these algorithms offer.

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Happy Learning!