The system that has been put together and designed by the Basque technology centre within the framework of the Ecopool project and has been “trained” at Tekniker’s facilities, enables real-time detection of people using indoor pools and improves energy management.
AI models have not only been designed to deliver highly complex and sophisticated solutions. Thanks to automatic learning techniques such as Deep Learning (i.e., in-depth learning based on convolutional neural networks - CNN), it is possible to obtain solutions that can perform, speed up and enhance routine tasks like counting people in a swimming pool and improve energy management.
The Tekniker technology centre has employed AI techniques within the framework of the Ecopool project to create a Deep Learning model embedded in a high-performance mini-PC that counts how many people are using an indoor pool on a real time basis.
More specifically, the resulting Deep Learning model is applied in the field of pool management to obtain a real-time estimate of how many people are using an indoor pool so that, as a function of the results obtained, water temperature readings can be generated in order to use less reagents and reduce the amount of power required for water purification purposes.
During the Covid-19 pandemic, the system has also been able to measure visitor-capacity at public and private pools.
With a view to developing this technology, the centre has followed a methodology that set out by generating a new data set that provides high resolution images. Similar data sets were searched to increase its variability as well to generalise and reinforce how the trained convolutional neural network responds.
The Lifeguard-io project, that uses computer viewing techniques as well as Machine Learning to detect individuals that are drowning provided the most suitable dataset solution to researchers involved in Ecopool.
In an attempt aimed at generating a people-counting model that can calculate for how long people swim, images are been labelled on which the presence of an object has to be indicated, a person in this instance, to allocate a label to said object.
Due to the fact that the image data set we had at our disposal was small in terms of variability, our expert teams focused on data augmentation, a technique that generates new data to improve the training of the neuronal network of the Deep Learning model by introducing a number of disruptions and variations in the original images such as scaling and rotations.
More specifically, Tekniker experts have applied image cropping techniques, rotations, translations, mirror effects, pixel perturbations, contrast changes, distortions between techniques, etc. to significantly enlarge the image dataset.
As regards the solution developed in Ecopool, the models have been trained on two systems featuring different computational capabilities. On the one hand, a PC equipped with a dedicated graphic card for the training of models at Tekniker’s was used to train the model in a local system. And, on the other, a cloud machine was hired to be used for model training purposes in a server with superior computational features.
The following organisations are involved in the Ecopool project: Tekniker, the engineering firm Dinycon Systems, the Gaiker technology centre and another company called Giroa.