Computer Vision Data Annotation: The Guide
💡 Giving sight to artificial intelligences: discover “Computer Vision” models and the importance of data annotation in training these models!
In Artificial Intelligence, the techniques known as ”Computer Vision“, belong to a field of applied AI that allows computers to derive meaningful information from digital images, videos, and other visual inputs, and to act or make recommendations based on that information. Computer Vision models allow computers to see, observe, and understand. This involves developing algorithms that can process, analyze, and understand images and videos.
The importance of Computer Vision annotation
Image annotation is a key process for anyone who wants to work on “Computer Vision” models. Annotation tools, such as CVAT and Labelbox, are essential to facilitate this process, making work faster and more efficient for teams of data scientists and artificial intelligence researchers. It consists in assigning labels (or labels) to the different parts of an image to help artificial intelligence algorithms recognize and understand objects and scenes.
CVAT, for example, is an open-source tool used for image and video annotation, offering a user-friendly interface and integration capabilities with machine learning frameworks. Among the methods that are commonly used is the use of ”Bounding Box“, which consists of surrounding objects with a “box” to locate them.
Overview of the main Computer Vision techniques
1. Classification
The technique of image classification involves training a computer to recognize patterns in images. It uses supervised learning algorithms to learn from labelled data and categorize images into predefined categories. For example: for several thousand fashion items such as handbags, precise identification of the product model.
2. Object detection
It is a technique for detecting and locating objects in an image. Object detection must be adapted to the specific needs of each project to ensure accuracy and efficiency. A ”Computer Vision annotation tool“is an interactive online tool that helps annotate videos and images as part of artificial intelligence projects, in particular those related to computer vision. Algorithms are used to identify objects, draw a “Bounding Box” around each object detected by a Data Labeler and labelled, and classify them into a predefined category.
3. Image segmentation
This technique consists of divide an image into distinct parts, or segments. Algorithms are used to identify the outlines of objects in an image and assign a label to each segment.
4. Object tracking
This technique is used to track objects in successive videos or images. Algorithms make it possible to locate the object in each frame of the video sequence by assigning a tag to it, and to follow it as it moves.
Most Common Computer Vision Use Cases
Image annotation is a crucial step in the development of computer vision applications. Effective solutions are needed to process and interpret visual data in a variety of use cases. For example, in the field of health, image annotation makes it possible to form models capable of detecting abnormalities in x-rays.
Detection of Patterns
Identification and location of objects in an image or video sequence. For example: identification of a brand logo on a series of product photos.
Face recognition
Identification and recognition of faces in an image, or recognition of certain facial expressions (for example: joy, sadness, doubt, etc.).
Medical image analysis
Extracting useful information from medical images bulky and not very structured. For example: annotation of instruments or anomalies.
Motion detection
Detecting motion in an image or video. For example: monitoring athletes on a field, to improve the video analysis experience of clubs.
Optical character recognition (OCR)
Recognizing text in an image or video. For example: recognition of specific information on invoices or pay slips.
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Some challenges of “Computer Vision” techniques and models: what are the challenges for Data Labelers?
Real-time data processing
“Computer Vision” algorithms require significant computing power, and processing large amounts of data in real time is sometimes complex.
Quality of images and videos (difficulties related to their resolution or low light)
Algorithms need to be able to accurately detect objects in low light conditions, which is made difficult due to the limited information available in these conditions. Open source tools like CVAT are used to improve the quality of annotations despite resolution and brightness challenges.
The data used to train the models, often prepared by Data Labelers, is often of lower quality because the initial images are ambiguous, which degrades the general quality of a data set that one would like to be close to the”Ground Truth”.
Occultation
Objects in a scene can be obscured or hidden by other objects, making it difficult for “Computer Vision” algorithms to identify them accurately.
What are the applications of Computer Vision in industry?
Computer vision plays an important role in industrial automation. Manufacturing industries, for example, integrate Computer Vision systems to optimize the production chain. These systems make it possible to monitor product quality in real time, detect defects, and improve overall process efficiency.
Automated inspection is one of the most widespread applications, where Computer Vision cameras and algorithms work together to verify product compliance with quality standards. This process, which was once done manually, is now made faster and more accurate thanks to Computer Vision technologies, reducing human errors and production costs.
In addition, these systems are essential in inventory management, allowing businesses to track the movement of goods in warehouses with increased accuracy. These applications show how Computer Vision is transforming not only high-tech sectors, but also more traditional industries, by improving efficiency, accuracy, and profitability.
Data Labeling Outsourcing: the key to improving your image analysis process?
Image annotation plays a very important role in the process of developing products based on “Computer Vision” models. Tools like CVAT are used by Data Labelers to facilitate the annotation process. The outsourcing of Data Labeling consists in entrusting the task of annotating images, videos or even files (text files, PDF files, etc.) to external experts specialized in data labelling.
These tools are essential for Data Scientists and AI researchers. This approach allows businesses to benefit from high-quality data, while focusing on their core business. The outsourcing of Data Labeling facilitates the obtaining of accurate and reliable results in the field of data collection as well as their processing.
Benefits of outsourcing data labeling
Outsourcing Data Labeling has many advantages for businesses looking to improve their data collection and processing process to build their AI products. Some of the main benefits include:
Specialized expertise
Outsourcing makes it possible to benefit from the expertise of professionals specialized in data labelling (at Innovatiana, it is our core business: we offer ethical outsourcing in Madagascar). These experts or Data Labelers have the skills to create accurate and consistent annotations, thus guaranteeing data quality for image analysis models.
Saves time and effort
Outsourcing Data Labeling allows companies to focus on their core activities, avoiding mobilizing internal resources for laborious and unengaging labeling tasks. This not only improves operational efficiency and reduces costs, but also makes efficient use of resources.
It is a shame for a Data Scientist or an AI Developer, whether they are an intern or have several years of experience, to spend most of their time assigning labels to data sets. This discourages them, and it is all the more a shame since it is not their core business. On the other hand, it is that of the Data Labelers!
Data accuracy and quality
Outsourcing Data Labeling ensures high-quality annotated data, which is essential for training efficient supervised learning models. Businesses can thus obtain more accurate and reliable results in their applications such as “Computer Vision” models, in particular.
In conclusion, outsourcing Data Labeling is an essential practice for Ensuring data quality in the world of applied artificial intelligence, and in particular for companies that want to exploit “Computer Vision” techniques to develop their products. By entrusting this task to specialized external service providers such as Innovatiana, businesses can benefit from high-quality annotated data to train their machine learning models.
