Our client, a renowned manufacturing business, asked Saffron Tech to build a ‘Supervisor Presence Detection’ System for their factory setup that can be used to track their on-ground staff members in some of the critical locations of the factory.
Almost every company on this planet witnesses a similar problem when they build certain rooms, cabins, halls, assembly lines, etc., for their employees, and they don’t show up for the task or go missing from the area.
An inefficient work process often leads to ineffective work outcomes. Situations like these can turn out to be a waste of time, resources, and money. Setting up a Supervisor Presence Detection System will help our client to maintain supervision of its workers in critical locations of the factory.
Saffron Tech collaborated with a leading manufacturing company and helped in developing and designing a ‘Supervisor Presence Detection System’ that can give them real-time visuals to detect the presence of their on-ground workers. We provided them with a wide range of services that were based on our Artificial Intelligence and Machine Learning knowledge.
Through this project, Saffron Tech helped them in smoothening their production workflow as such that they are now able to deliver their finished products at full tilt, creating better-manufactured product quality and accurate record-keeping.
The primary purpose of this project was to provide a real-time Supervisor Presence Detection System to our client so that the administrative authorities of the company could easily monitor whether a staff member is present where he or she is supposed to be, in a specific industrial area or a critical location. This system can also be used for ground control staff supervision and for areas where the stock is loaded/unloaded.
The presence detection system is capable of detecting the presence of workers inside a room or a hall and organizes the acquired images in a storage service. It is able to store big databases containing diverse images of certain objects, areas, or workers.
Saffron Tech proposed a Supervisor Presence Detection system that is powered by Artificial Intelligence and Machine Learning. This system can be used for monitoring the workers, whether they are present in a specific location of the factory or not.
This system helped them in smoothening their production workflow so that they could deliver better manufactured product quality at high speed.
Python Language – Saffron Tech used ‘Python code’ as it is well-suited for A.I. because Python makes the construction of AI models much simpler. It brings a broad choice of systems and libraries; it has also been used by popular brands like LensKart. It can perform an array of complex AI errands and help in constructing AI models at a rapid speed.
Deep Learning Frameworks – Python exhibits a diverse arrangement of libraries for computer reasoning and Artificial Intelligence. Therefore, for deep framework learning, Saffron Tech used Pytorch, Tensorflow, and Keras to base the feature.
Numerical Computation – We used NumPy for boosting information examination and for establishing optimum logical registering. We also used Pandas for information examination. The combination of NumPy and Pandas enabled us to bring our clients the ultimate structure they required.
Machine Learning – For the Machine Learning pipeline, we used sci-kit-learn, a learning library for Python Language which offered various dynamic tools for machine learning such as regression, classification, clustering, and more.
Image Processing – We operated image processing with OpenCV or Open Source Computer Vision Library because it provides more than 2500+ optimized algorithms. Skimage for image pre-processing. We also used PIL, to provide image editing functionalities.
DevOps – We used DevOps with Docker because it makes load balancing simpler with pre-installed service concepts, AWS for Cloud Formation and service integration, and GCP or Google Cloud Platform.
Our Supervisor Presence Detection System in this manufacturing company was used to detect on-ground staff members and provide real-time visuals of the human presence directly to the admin. We were able to build a robust system that was compatible with all devices of note.
This system serves many purposes because it has multiple features; the admin can count the total number of workers arriving in a specific location or going out, it can be used to analyze the work behavior of the employees. It can also be used for security purposes, for eg. detecting intruders in a critical space of the factory.