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Author (up) Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu edit  doi
isbn  openurl
  Title Waste Classification with Small Datasets and Limited Resources Type Book Chapter
  Year 2022 Publication ICT Applications for Smart Cities. Intelligent Systems Reference Library Abbreviated Journal  
  Volume 224 Issue Pages 185-203  
  Keywords  
  Abstract Automatic waste recycling has become a very important societal challenge nowadays, raising people’s awareness for a cleaner environment and a more sustainable lifestyle. With the transition to Smart Cities, and thanks to advanced ICT solutions, this problem has received a new impulse. The waste recycling focus has shifted from general waste treating facilities to an individual responsibility, where each person should become aware of selective waste separation. The surge of the mobile devices, accompanied by a significant increase in computation power, has potentiated and facilitated this individual role. An automated image-based waste classification mechanism can help with a more efficient recycling and a reduction of contamination from residuals. Despite the good results achieved with the deep learning methodologies for this task, the Achille’s heel is that they require large neural networks which need significant computational resources for training and therefore are not suitable for mobile devices. To circumvent this apparently intractable problem, we will rely on knowledge distillation in order to transfer the network’s knowledge from a larger network (called ‘teacher’) to a smaller, more compact one, (referred as ‘student’) and thus making it possible the task of image classification on a device with limited resources. For evaluation, we considered as ‘teachers’ large architectures such as InceptionResNet or DenseNet and as ‘students’, several configurations of the MobileNets. We used the publicly available TrashNet dataset to demonstrate that the distillation process does not significantly affect system’s performance (e.g. classification accuracy) of the student network.  
  Address September 2022  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title ISRL  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-031-06306-0 Medium  
  Area Expedition Conference  
  Notes LAMP Approved no  
  Call Number Admin @ si @ Serial 3813  
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