💡 Giving artificial intelligences sight: discover "Computer Vision" models and the importance of data annotation in training these models!
In Artificial Intelligence, the techniques known asComputer Vision"belong to a field of applied AI that enables computers to derive meaningful information from digital images, videos and other visual inputs, and to act or make recommendations based on this information. Computer Vision models enable computers to see, observe and understand. This involves developing algorithms capable of processing, analyzing and understanding images and videos.
The importance of annotation Computer Vision
Image annotation is a key process for anyone wishing to work with Computer Vision models. Annotation tools such as CVAT and Labelboxare essential to facilitate this process, making work faster and more efficient for teams of data scientists and artificial intelligence researchers. It involves assigning labels to 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. Commonly used methods include the use ofbounding box"which consists in 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 labeled data and classify images into predefined categories. For example: for several thousand fashion items such as handbags, precise identification of the product model.
2. Object detection
This is a technique for detecting and locating objects in an image. Object detection must be adapted to the specific needs of each project to guarantee accuracy and efficiency. A "annotation tool for Computer Vision"is an interactive online tool that helps to annotate videos and images as part of artificial intelligence projects, particularly those related to computer vision. Algorithms are used to identify objects, draw a "Bounding Box" around each object detected by a Data Labeler and classify them in a predefined category.
3. Image segmentation
This technique involves dividing an image into distinct parts, or segments. Algorithms are used to identify the contours of objects in an image and assign each segment a label.
4. Object tracking
This technique is used to track objects in successive videos or images. Algorithms are used to locate the object in each frame of the video sequence by assigning it a tag, and to track it as it moves.
Common Computer Vision use cases
Image annotation is a crucial step in the development of computer vision applications. Efficient solutions are needed to process and interpret visual data in a variety of use cases. For example, in the healthcare field, image annotation is used to train models capable of detecting abnormalities in X-rays.
Pattern detection
Identify and locate objects in an image or video sequence. For example, identifying a brand logo in a series of product photos.
Facial recognition
Identification and recognition of faces in an image, or recognition of certain facial expressions (e.g. joy, sadness, doubt, etc.).
Medical image analysis
Extraction of useful information from large, unstructured images. For example: annotation of instruments or abnormalities.
Motion detection
Detection of movement in an image or video. For example: tracking athletes on a pitch, to improve the video analysis experience of clubs.
Optical character recognition (OCR)
Text recognition in images and videos. For example: recognition of specific information on invoices or pay slips.
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Some challenges of Computer Vision techniques and models: what challenges for Data Labelers?
Real-time data processing
Computer Vision algorithms require considerable computing power, and real-time processing of large quantities of data can be complex.
Image and video quality (difficulties related to resolution or low brightness)
Algorithms must be able to accurately detect objects in low-light conditions, which is made difficult by the limited information available in these conditions. Open source tools such as CVAT are used to improve annotation quality despite the challenges of resolution and brightness. The data used to train the models, often prepared by Data Labelers, are often of lower quality because the initial images are ambiguous, which degrades the overall quality of a dataset that we would like to be close to "ground truth".ground truth".
Occultation
Objects in a scene may be obscured or hidden by other objects, making it difficult for computer vision algorithms to identify them accurately.
What applications does Computer Vision have 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 can monitor product quality in real time, detect defects and improve overall process efficiency.
Automated inspection is one of the most widespread applications, where cameras and Computer Vision algorithms work together to verify product compliance with quality standards. This process, once carried out manually, is now made faster and more accurate by Computer Vision technologies, reducing human error and production costs.
In addition, these systems are essential in inventory management, enabling companies to track the movement of goods in warehouses with greater precision. 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 development process of products based on Computer Vision models. Tools such as CVAT are used by data labelers to facilitate the annotation process. Outsourcing Data Labeling involves entrusting the task of annotating images, videos or even files (text files, PDF files, etc.) to external experts specialized in data labeling. These tools are essential for data scientists and AI researchers. This approach enables companies to benefit from high-quality data, while concentrating on their core business. Data Labeling outsourcing makes it easier to achieve accurate and reliable results in data collection and processing.
The benefits of outsourced data labeling
Data Labeling outsourcing offers many advantages for companies looking to improve their processes for collecting and processing data to build their AI products. Here are some of the key benefits:
Specialized expertise
Outsourcing allows you to benefit from the expertise of professionals specialized in data labeling (at Innovatianathis is our core business: we offer ethical outsourcing in Madagascar). These experts, or Data Labelers, have the skills required to produce accurate and consistent annotations, thus guaranteeing data quality for image analysis models.
Save time and effort
Outsourcing data labeling enables companies to concentrate on their core business, avoiding the need to mobilize internal resources for laborious, time-consuming labeling tasks. Not only does this improve operational efficiency and reduce costs, it also means that resources can be used more effectively. It's a shame for a data scientist or AI developer, whether a trainee or with years of experience, to spend most of their time assigning labels to data sets. This discourages them, and is all the more unfortunate as it's not their core business. Data Labelers, on the other hand, are!
Data accuracy and quality
Data Labeling outsourcing guarantees high-quality annotated data, essential for training high-performance supervised learning models. As a result, companies can achieve more accurate and reliable results in their applications of Computer Vision models, for example.
In conclusion, outsourcing Data Labeling is an essential practice for guaranteeing data quality in the world of applied artificial intelligence, and in particular for companies wishing to exploit "Computer Vision" techniques to develop their products. By entrusting this task to specialized external service providers such as Innovatianacompanies can benefit from high-quality annotated data for training their machine learning models.
