  The ever-increasing amount of global refuse is overwhelming the waste and
recycling management industries. The need for smart systems for environmental
monitoring and the enhancement of recycling processes is thus greater than
ever. Amongst these efforts lies IBM's Wastenet project which aims to improve
recycling by using artificial intelligence for waste classification. The work
reported in this paper builds on this project through the use of transfer
learning and data augmentation techniques to ameliorate classification
accuracy. Starting with a convolutional neural network (CNN), a systematic
approach is followed for selecting appropriate splitting ratios and for tuning
multiple training parameters including learning rate schedulers, layers
freezing, batch sizes and loss functions, in the context of the given scenario
which requires classification of waste into different recycling types. Results
are compared and contrasted using 10-fold cross validation and demonstrate that
the model developed achieves a 91.21% test accuracy. Subsequently, a range of
data augmentation techniques are then incorporated into this work including
flipping, rotation, shearing, zooming, and brightness control. Results show
that these augmentation techniques further improve the test accuracy of the
final model to 95.40%. Unlike other work reported in the field, this paper
provides full details regarding the training of the model. Furthermore, the
code for this work has been made open-source and we have demonstrated that the
model can perform successful real-time classification of recycling waste items
using a standard computer webcam.
