Computer Vision is a field that uses cameras and computers to mimic the functions of the human eye, including tasks such as recognition, tracking, and measurement. Additionally, it also encompasses the use of computers to process images to make them more suitable for human or instrument detection. In computer vision, machine learning or deep learning techniques are often used to achieve various goals.
Machine learning is a technology that uses algorithms and artificial intelligence to allow machines to learn from data and make decisions based on that learning. Machine learning has a history of several decades, including disciplines such as probability, statistics, and numerical analysis. The method by which machine learning solves problems is by extracting patterns from data and using those patterns to predict unknown data. In recent years, significant progress has been made in various computer vision tasks based on neural networks and deep learning using large amounts of data in machine learning.
For example, in the ImageNet image recognition competition held in 2015, a neural network with 152 layers was able to reduce the error rate of computer image recognition to 3.6%, surpassing the average error rate of 5% for humans, showing that machines can better solve image-related problems than humans.
Breakthrough to a new "vision" of humanity
In recent years, with the constant progress of technology, computer vision technology has once again attracted widespread attention. Just as human vision enables us to understand and interpret the world around us, computer vision also allows machines to observe and interpret the surrounding environment. To achieve this goal, machines rely on technologies that help them extract meaning from visual data and understand the content they see.
In computer vision tasks, object detection and instance segmentation are the most common and effective techniques. These techniques assist computer vision applications in obtaining usable information from images and using that data for image processing and automation of object detection tasks with artificial intelligence.
Although these two techniques are closely related, there are subtle differences between them. In this article, we will show you the differences between these techniques to avoid confusion and explain how each technique works, let's begin.
Object Detection V.S. Instance Segmentation
Object detection uses image processing and recognition in related fields to identify which target objects are present in an image, determine their semantic categories, and mark their location within the image.
Instance segmentation is a complex form of image segmentation that helps to separate annotated examples from their belonging category. For example, an object detection application for detecting people would separate individuals and apply different colors to each person, displaying them as distinct instances. This technique is particularly useful in complex visual environments where many similar objects exist and need to be distinguished. In simple terms, instance segmentation is the combination of object detection and semantic segmentation. It detects the target in the image (object detection) and then labels each pixel (semantic segmentation).
4 Things You Need to Know
Why instance segmentation and object detection are important?
Instance segmentation has many uses in fields such as medicine and satellite imagery. In pathological images, there are often a large number of differently shaped cell nuclei around the cytoplasm, and instance segmentation can detect and distinguish the cell nuclei for granuloma treatment; satellite images often contain small and complex objects that are difficult to distinguish because of their proximity. Instance segmentation uses network architecture to achieve better results from satellite images and also plays an important role in monitoring marine pollution and detecting ships for maritime safety.
Object detection is used in autonomous cars, and advanced driver assistance systems use object detection technology to detect pedestrians and navigate in lanes, thus improving driving safety. In addition, object detection is also a powerful monitoring and image retrieval system that can enhance the safety of a place.
How do they work?
Instance segmentation is a method of finding similar objects and identifying each object from the same class as a single instance. To train a model for this segmentation, we need to store descriptions of the instance in a database. At runtime, the system uses "confidence scores and threshold" to check if the instance exists.
Object detection uses a variety of techniques. For object detection through deep learning, there are two main methods:
Using pre-trained object detectors: Object detection performed through deep learning uses transfer learning. This method allows you to use a pre-trained model and fine-tune it for your application. Using this method, you can get faster results because the model was pre-trained on thousands of images.
Creating and training a custom object detector: Training a custom object detector involves starting from scratch. You need to develop a network architecture to learn to detect objects in a specific application. This method allows you to have better control over the model and may result in better performance, but it also requires more time and resources.
When to use it?
When you need to distinguish multiple instances of the same class in an image, instance segmentation comes in handy. Especially in complex visual environments, when a large number of similar objects need to be distinguished from each other, instance segmentation becomes particularly important.
If you need to locate and classify objects in an image, object detection is your best choice. It is widely used in various scenarios, including autonomous vehicles, monitoring systems, and image retrieval, and provides great help in these situations.
Main differences
Although instance segmentation and object detection are related and often used together, there are some key differences between these two techniques:
Object detection creates bounding boxes around objects, while instance segmentation also identifies each instance within those bounding boxes.
Instance segmentation is typically more complex and computationally expensive than object detection.
Conclusion
Object detection is a technique in computer vision that allows computers to find specific objects within images and distinguish between different objects. For example, if you have a picture and you want the computer to find dogs in the image, object detection can help you achieve that goal.
Instance segmentation is another technique in computer vision that is used to distinguish between different objects within an image. For example, if you have a picture with many dogs and want the computer to distinguish between each dog, instance segmentation can help you achieve that goal.
Although object detection and instance segmentation may appear similar, they are distinct. Object detection is used to find specific objects within an image, while instance segmentation is used to distinguish between different objects within an image. If you want the computer to find specific objects within an image, you would use object detection; if you want the computer to distinguish between different objects within an image, you would use instance segmentation.
In computer vision applications, although there are subtle differences between these two techniques, they both help machines extract meaning from visual data to better understand and interpret the world around them. Understanding the difference between these techniques can be very helpful if you need to choose the right method for a specific application.
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