Giving artificial intelligences sight: discover the "Computer Vision" models
In Artificial Intelligence, techniques known as "Computer 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 image annotation
Image annotation is a key process for those wishing to work with Computer Vision models. It involves assigning labels to different parts of an image to help artificial intelligence algorithms recognize and understand objects and scenes. Commonly used methods include"bounding box", which consists of surrounding objects with a "box" to locate them.
Overview of the main Computer Vision techniques
1. Classification
This technique involves training a computer to recognize patterns in images. It uses supervised learning algorithms to learn from 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. Algorithms are used to identify objects, draw a "Bounding Box" around each object detected by a Data Labeler and labelled, 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
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 medical 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)
Recognition of text in an image or video. For example: recognition of specific information on invoices or pay slips.
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. The data used to train the models, often prepared by Data Labelers, are often of lower quality, as the initial images are ambiguous, degrading the overall quality of a data set that we would like to be close to"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.
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. Data Labeling outsourcing involves entrusting the task of annotating images, videos or even files (text files, PDF files, etc.) to external experts specialized in data labeling. 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, 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 Innovatiana, this is our core business: we offer ethical outsourcing in Madagascar). These experts, or Data Labelers, have the skills needed to produce accurate and consistent annotations, 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, Data Labeling outsourcing 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 like Innovatiana, companies can benefit from high-quality annotated data for training their machine learning models.
