What is Object Detection?

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A camera can capture a scene as an image or a video. However, they can’t process that image or video and identify what is in it like we do. Object detection is a technique computers use to identify, locate, and count objects in an image or video. 

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It is a part of computer vision technology. This technology is used in various industries, including logistics, healthcare, manufacturing, and others.

Object Detection Models and Architecture

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Object detection models are pretrained programs that can recognize and locate objects. They follow an architecture, a design that is used to process images.  

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The system used for detecting objects processes an image or video in three key steps. First, it takes an image or scene and extracts the feature from it. It breaks down the image into several parts and then collects their shape, color, and textures.

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Then, the system compares those key details with the thousands or billions of images using its training data. It classifies the object based on the patterns of its model. Finally, it identifies the object and locates its position by drawing a mark around it. This mark appears as a box, known as a bounding box in computer vision. 

Common Architectures

There are different object detection architectures, including the following:

• R-CNN (Region-based Convolutional Neural Network): This is one of the earliest models of object detection. It breaks an image into multiple regions and checks each one for objects.
  
  
• Faster R-CNN: An improved version that detects objects faster while maintaining accuracy.
  
  
• YOLO (You Only Look Once): Processes the entire image at once and is widely used for real-time detection.
  
  
• SSD (Single Shot MultiBox Detector): Balances speed and accuracy, making it useful for mobile applications.

How to Choose the Right Model

An object detection model should be chosen based on the task to be performed. Some models are best suited for fast results, while others are designed to deliver more accurate results. For example, self-driving cars need faster real-time results, and accuracy is most important. 

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Application of Object Detection in Different Sectors

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Many tasks can be automated using object detection, including inventory management, diagnosis, and traffic management. Here is how this technology can be used in different industries:

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Manufacturing

The production of defective products can lead to increased waste and financial losses in the manufacturing industry. Object detection technology can identify the presence of specific materials used in production. It can prevent the production of defective products early in the manufacturing stage. 

Healthcare

Doctors can identify and locate the presence of tumours and fractures with this technology. This technology can detect patterns from MRIs, X-rays, and scans and give useful insights for diagnosis. 

Transportation

Smart cities can use this technology for traffic management by detecting the number of vehicles on the road. It is used in self-driving cars to detect the presence of other vehicles or pedestrians. 

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Security and Surveillance

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It can be used to prevent theft, unauthorized access, or suspicious activities. For example, the security camera of a store can detect someone removing an object from the shelves and send instant alerts to the store staff. 

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Retails

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This technology can be used to count the number of objects on store shelves. It can also be used for automatic stock management or replenishment. Stores can avoid stockouts and have a better store organization with this technology. 

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How Vision AI is Changing the Future of Object Detection

Object detection systems use computer vision to process images. These systems excel at image detection, but they often require retraining when environmental conditions change. But using Vision AI, these problems can be solved. 

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At Tiliter, we use Vision AI-powered agents for object detection. For example, our object validator agent can detect and identify if any object is present in an image. We also have an object counter agent that can count the number of objects present in an image. 

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The object detection system, powered by Vision AI, can detect objects with great accuracy, even under challenging conditions, such as poor lighting and at an angle. These systems don’t need retraining when the object detection condition changes. 

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Want to learn more about the difference between computer vision-powered object detection and AI-powered detection? Read our previous blog here.

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Conclusion

Performing manual inspection to verify the presence of an object can be time-consuming and deliver inaccurate results. Detecting objects with computer vision and Vision AI can solve that problem. Industries from various sectors are attempting to automate their workflows with technologies like object detection. 

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Tiliter is working to make Computer Vision and Vision AI accessible for everyone. We offer Vision AI Agents and other AI-powered tools that can perform visual tasks faster and with greater accuracy. 

👉 Ready to automate your workflow with Tiliter’s Vision AI? Get in touch with us today.