Computer vision is defined as the use of computers to automatically perform tasks that can be performed by the human visual system. It refers to the process through which a computer can recognize and interpret images using artificial intelligence (AI) algorithms, and later construct an acceptable result of the analysis that the computer “understands.”
As a type of artificial intelligence, it can classify, identify, verify and detect objects, that is, this technology enables machines to visually interpret the world around them. Typically, this visual data is in the form of photos or videos, but can also include data from thermal and infrared cameras.
To accomplish this task, computers use digital cameras, develop analog-to-digital conversion (ADC) processes as well as digital signal processing (DSP) functionality. Additionally, they use an algorithm (machine learning algorithm) that must be trained until it can interpret the images well.
Computer vision is already used to count cars and recognize high-traffic areas, or to check that factory employees are wearing masks, for example. Several of the main applications are in manufacturing, resource extraction and construction. In these fields, computer vision is useful for automating tasks including defect detection, quality control, predictive maintenance and remote measurement.
In the healthcare sector, it assists with remote measurement and diagnosis. And in retail and security, it allows the monitoring of humans and their behavior in real time in order to understand purchasing behavior or detect illegal activity. Thanks to computer vision, Amazon was able to completely eliminate the physical checkout process in its Amazon Go stores: once customers are scanned at the store entrance, they can wander, pick up the items they want and then leave without having to stand in line to pay; the cameras track what the customer selects and then the cost of the items is automatically charged to their account. Moreover, this technology – specifically facial recognition – can also be used to identify individual customers in order to provide them with personalized recommendations and rewards.
Computer vision is commonly used to speed up real-time analysis of satellite images and assess which regions are affected by natural disasters or human activity. It is also used to track wildlife or monitor crops or livestock, to name just a few applications. In agriculture it has a variety of uses, including detecting weeds, diseases, and pests, soil testing, detecting water leaks, tracking animals, and categorizing products when harvested.
Computer vision is also used to analyze electronic components, for signature identification, optical character recognition, object and pattern recognition, material inspection, among many other uses.
Machine learning techniques are used to train the algorithm to better recognize images. Constant training greatly increases the prediction rate of the machine learning model. The algorithms use the training data to form relationships, gain understanding, make judgments, and evaluate their reliability.
It should be noted, however, that there is an undeniable link between properly annotated data and the success of an artificial intelligence (AI) project: one study found that preparing, cleaning, and labeling data takes up to 80% of the development time for such a project. The importance of data annotation is due to the fact that the slightest inaccuracy could have serious consequences. Self-learning models require a large amount of annotated data to train with before going live.
When it comes to computer vision, image annotation is crucial. In this process tags are assigned to specific tasks in photos. Photographs should be annotated for best results to prepare highly accurate training data. But this task cannot be completed by just anyone: experience and contextual understanding are required, as well as mastery of specific methodologies and tools. In short, human resources are critical components of the data annotation process, particularly when performing specific tagging tasks.
When it comes to image annotation, the instinct may be to automate. However, unsupervised machine learning is not yet suitable for automatic annotation of images because machines have difficulty mapping and identifying the objects in them. In this case, manual human-driven image annotation is more efficient, and on the other hand offers a scalable option with fast response times. Ultimately, it is about finding the balance between automation and human intervention that best suits the needs of the specific project.
Depending on the case, it will also be necessary to decide whether the annotation and labeling of data should be done by an internal team or whether this job should be outsourced. Generally speaking, it is most beneficial to entrust this job to an organization that specializes in this subject for several reasons, including the fact that it is prepared for the large volumes of data involved in short-term projects. Also, if this job is performed by an engineer, it can be expensive, since it involves many hours of labor.
At Arbusta we see ourselves as partners of organizations undertaking these data annotation and algorithm training processes, in order to help them benefit from the advantages of computer vision and other disciplines derived from AI. We provide agile and competitive IT services for the digital transformation processes of companies that require hybrid human-machine solutions. We focus on processes that require human intervention, that is, the incorporation of inputs throughout the automatic learning cycle of an algorithm, to accelerate its development and improve it, as well as data collection services (Data set collection) and training (Data Annotation) of AI models.