Your definitive guide to object detection

Isn’t it fascinating that computers would possess human-like identifying traits and would be able to detect the position of objects? Imagine how a self-driving car could detect people crossing and slow down the car using an AI algorithm.

Let's explore various state-of-the-art computer vision algorithms that are going to make our lives a whole lot easier.

What is Computer Vision Object Detection?

Consider, we have different classes of objects (like robots, plants, cars, humans, etc.).

Now, we want to detect different instances of such objects in digital images of photos or video clips. Object Detection helps us do that.

Object Detection, a key aspect of Computer Vision algorithms, aids us in detecting where the object is and what it is.

It has many applications ranging from face detection and faces recognition to image retrieval and video surveillance.

Why is Object Detection important?

Object Detection is one of the most important steps in Computer Vision. This is because the fundamental step in recognizing a person or object is detecting whether it’s a person or a thing.

Many Computer Vision tasks like pedestrian detection, face detection, pose detection, or number-plate recognition are based on Object Detection.

It can impact many sectors and can influence many futuristic technologies like crowd counting, self-driving cars, video surveillance, face detection or even anomaly detection.

You may wonder what’s special in Object Detection, as we had developed image recognition and image segmentation technologies many years ago. The difference is that Object Detection gives us the ability to locate the objects whereas the other two technologies just find the class of an object and understand a scene’s elements respectively.

With the location of an object in an image or video, we can now track it and even count it.

These unique capabilities of Object Detection make it a crucial step in Artificial Vision or Computer Vision.

Object Detection and Deep Learning

Deep Learning is a technology used by computers to gain knowledge as we humans do.

It’s a specialized form of machine learning wherein we provide a dataset based on which the machine acquires its learning. This learning subsequently helps the machine to reach a decision.

But the important aspect of deep learning lies in the capability it provides the machines, i.e., learn from their own mistakes, hence the term “Deep learning”.

But how will the knowledge be applied?

Photo by Hamster3d via Getty Images

You must have heard about video surveillance. But ever thought of tracking people in a video?

The most vital step in tracking a person in the real-time video is to detect it.

With deep learning networks and algorithms, object detectors and trackers are improving every day, paving the way to breakthroughs in this field.

How does Object Detection work?

Object Detection can be performed in two ways, either by a traditional technique of Image Processing or by a modern method of using Deep Learning.

Let’s explore both the methods with their pros and cons:

1. Image Detection and Processing Using AI

This is a technique that requires no historical data or any special supervised training to detect objects. The benefit of such a technique is that it does not require humans to manually label historical data in form of annotated images for training purposes.

But this method takes a hit in ambiguous situations involving partially hidden objects, shadows, or situations without unicolor background.

2. Deep Learning

One of the most advanced methods involving supervised training using deep learning limits the performance as it depends on the computation power of GPUs.

With supervised training received under humans and powered with historical data, the accuracy of object detection is far higher than the traditional method of Image Processing.

The positive benefit of using deep learning lies in its capability of detecting objects even in ambiguous situations of occlusion, complex scenes, and challenging illumination.

However, it’s still not perfect, as the accuracy is achieved via labor extensive exercise of annotating images, used as historical data for supervised training. Such datasets are quite expensive.

The requirement for training is so vast that a set of 500,000 annotated images used to train a Deep Learning powered Object Detection Algorithm is considered a small dataset.

Applications of Object Detection

Being the core of every vision-based AI software, it plays a crucial role in understanding a scene and thus has application in security, transportation, medical, and military use cases.

Well, it’s been extensively used to provide computers with a human-like vision to automate tedious and mundane tasks and develop AI-powered products and services.

Let’s understand the details about the use cases of Object Detection:

1. Object Detection in the Retail Industry

The retail industry uses object detection for counting people. But how is it going to benefit them?

Well, the answer is simple- to understand consumer behavior and buying mindset.

Photo by ismagilov from Getty Images

By tracking the customers, we can gather information regarding how customers spend their time buying a particular product and the average footfall a product receives.

We can provide the best customer experience by optimizing the store layout. We can even make operations more efficient like detecting queues to reduce wait times.

2. Real Time Object Detection for Autonomous Driving

The applications of this technology in autonomous vehicles are endless.

The most crucial use cases include detecting and recognizing pedestrians, different traffic signs, and oncoming vehicles or obstacles.

As these vehicles are autonomous, they must have the capability to identify, locate, and track objects around them to move through the world safely.

Now, perceiving environmental and surrounding threats is easier than before, thanks to Object Detection.

3. Anomaly Detection

The use case that best describes this application lies in the agriculture sector.

With custom object detection models, it’s possible to identify and locate potential instances of plant disease. This will allow the farmers to detect threats to their products that they cannot observe with the naked eye.

Livestock farming industry can use object detection for counting animals and monitoring them. This makes the processes easier, faster and more accurate than before.

4. Security and Surveillance

One of the most important applications, video surveillance uses this technology to either detect people in restricted or dangerous areas. Video surveillance methods also use computer vision to automate inspection tasks on remote or hard to reach locations.

Officials use it to prevent suicides or dangerous encounters by detecting people if there’s a danger.

5. Healthcare and Medicine

Object Detection has breakthrough applications in the field of medicine as it can detect potential diseases and make us capable of understanding the human body better.

The medical community uses it to diagnose diseases by studying images, CT and MRI scans, and photographs, thus helping it thrive.
Photo by Kiquebg from Pixabay

Conclusion

Object Detection is a crucial step in Computer Vision systems. It has received great attention in recent years with innovation in Deep Learning methods.

Many AI startups like Tooliqa and Megvii are using Object Detection technology to revolutionize some unique industry applications.

Object detection is going to stay in the future with its widespread use in many sectors as we are yet to witness many of its innovations.

Read also: A guide to object tracking | Insights - Tooliqa

Tooliqa specializes in AI, Computer Vision and Deep Technology to help businesses simplify and automate their processes with our strong team of experts across various domains.

Want to know more on how AI can result in business process improvement? Let our experts guide you.

Reach out to us at business@tooli.qa.

FAQs

Quick queries for this insight

How doe object detection work?

Detecting objects in images and videos is a complex task for computers. It requires understanding the scene, identifying the relevant objects, and then classifying them. Although there are many different ways to approach object detection, most systems follow a similar general process. First, the system extracts low-level features from the image or video data. Next, the system groups these features into potential objects. Finally, the system uses cues from the scene, such as the size and location of objects, to identify each object and its category. This process is typically repeated at multiple scales to ensure that all objects are detected, regardless of their size or position in the image.

Why is object detection important for the present and future?

Object detection is a critical task in many fields, including robotics, autonomous driving, and security. The ability to accurately detect and identify objects is essential for machines to interact effectively with their environment. For example, autonomous vehicles need to be able to detect other cars, pedestrians, and obstacles in order to navigate safely. Security systems use object detection to identify intruders and potential threats. And industrial robots rely on object detection to locate and grasp parts. In each of these cases, object detection can have a significant impact on performance and safety. As machine learning techniques continue to improve, we can expect object detection to become increasingly accurate and widespread.

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