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The sustainability goals of the aviation sector ask for innovative ways to reduce CO2. One of the routes, is improving the alignment of fuel, speed and flying height to the weight of the airplane. The better the weight is predicted, the better all parameters can be calculated. Passengers weight is the only weight variable not being measured, but estimated by calculating the sum of the average weight of men, women and children on board. This means the biggest improvement can be obtained by reducing the inaccuracy of the passengers weight estimate. Our challenge was to improve the prediction of passengers’ weight without disturbing the boarding process or violating people’s privacy.
people voluntarily recorded
frames of data
hours of data collection
To find out someone’s weight without using a weighing scale, we assumed the best route would be to predict a person’s body volume with a depth sensor. Research has shown that the use of this technology enabled the predictions of one’s BMI by analyzing a face. Our reason to believe there would be equal possibilities in the area of weight prediction.
We started the prototyping phase by collecting weight and visual data on the airport. The main challenge in creating the prototype was how to transform the camera images into data to train our AI model. We created features based on the images, like length, age and volume. We tried and tested until we came to a model that predicts weight relatively well.
Reduced weight deviation of 88%
Computer vision can be used for weight calculation
Estimation of six figure business cost savings
