Semi-supervised training of deep neural networks for weather events detection from cameras
Giacomo Blanco
Semi-supervised training of deep neural networks for weather events detection from cameras.
Rel. Fabrizio Lamberti, Lia Morra. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2020
Abstract
Estimating the presence of water on the road is a crucial application when targeting safety-related issues concerning the operational life of a road stretch. Having the capability of deploying a real-time road conditions classifier may represent a powerful way to reduce the number of car accidents caused by bad weather conditions and in general may help making the drivers more aware of the surrounding environment while driving. This work describes the realization of a Machine Learning model able to estimate road conditions from on-site camera images. The project has been carried out within a collaboration between Politecnico di Torino university and Waterview srl.
This work describes the construction and the partial annotation of the training dataset starting from the large amount of data collected by the company, but its main focus is the application of self-supervised and semi-supervised techniques for exploiting a large amount of unlabelled data in a supervised task
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