Computer Vision: the two questions for five success stories

The sense of sight has played a vital role in the adaptation and survival of species. In fact, nature has solved this challenge from different evolutionary branches, reaching very similar solutions. For example, the morphology of the eyes has been adapted in each species to its environment of survival. Thus, for felines the pupil is vertical, for herbivores it is more horizontal and for mice or humans it is rather circular.

And as in the animal world, companies can also develop their sense of sight, through computer vision or artificial vision, adapting their capacity to different needs to compete and survive . For each problem, different processes can be designed with their own requirements, such as the resolution of the image, which can be low to take a temperature or very high if we are looking to diagnose a pathology.

How does artificial vision generate value?

The artificial vision systematizes information available in the images, taking different techniques to characterize and interpret its contents. The main techniques or algorithms are:

•                     Object detection (star formations)

•                     Object Classification (Label)

•                     Segmentation within the image to which object class each pixel belongs.

By characterizing the content of the images, we can then obtain derived metrics such as the number of objects of a certain class, such as the number of trees on a farm or the number of vehicles (and their brands) on a street. By being able to position the objects and their disposition over time, we can have other derived metrics such as the speed of the object, its trajectory or, in the case of people, it allows us to identify the pose and characterize their reaction to a stimulus.

The use cases in which artificial vision is adding value are multiple, from cases applied to science, such as the monitoring of in vitro fertilized embryos, to other more commercial applications such as the number of people who pass in front of an establishment. The sectors are also multiple, finding applications in sectors as diverse as sports, insurance, health, e-commerce , agriculture or construction. And the functions that it impacts are extensive, all those that use images.

How to ensure the success of a Computer Vision project?

To successfully implement a Computer Vision project, it is important to clearly identify the use case, what benefit the organization expects to obtain and from what process. Our experience has shown us that these projects are multi-user, multi-role and with multiple technological aspects that add complexity to their management. At Enzyme we have developed a methodology and the eSTAR.ai platform to ensure that this type of project comes to fruition. Its main steps are:

•                     Definition of the use case, target users and business case

•                     Launch of project generation of data set and labeling

•                     Algorithm training for the business objective

•                     Putting into production, monitoring the quality of your results

The benefits that our methodology provides when implementing these Artificial Vision projects in companies are clear, facilitating customization, improving the time to market of projects, saving time and ensuring the capitalization of project development by all organization teams.

Artificial Vision applied in five success stories

Case 1. Diagnosis of pathology . Applied to the detection of respiratory diseases in pig farms. It allows increasing the number of animals evaluated to determine preventive actions such as treatment and selection of specimens at a lower cost and to maximize the efficiency of the use of vaccines on farms. Enzyme has been recognized with the IBM Beacon Award of 2021 in AI and innovation of this solution.

Case 2. Exhibitor or linear analysis . To ensure compliance with the agreed service levels on how and where to display products on the distributor's shelf and obtain the maximum amount of information from in-store displays to correlate with sales volume.

Case 3. School fraud . Facial recognition and verification of the identity of the students who take the exams remotely.

Case 4. Quality control . from the appearance of the product or packaging. Production standards are set and the different rules and metrics that determine the quality of the product are visually verified. Quality control best example is TC Bolts

Case 5. Medicine . Identify the morphological characteristics of a tissue to guide the doctor on the areas with the highest probability of pathological formations.

Artificial vision is a key resource that companies have to improve their operations and the relationship with their customers. By having the appropriate methodology, we can make it easier for companies to implement Computer Vision projects that generate clear profitability through more income or lower costs for companies.

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