Abstract:
Plastic waste segregation is important for optimizing
the recycling process and minimizing the negative effects
associated with improper disposal of plastic waste. Nonetheless,
most of the smart waste management systems are designed
mainly for general waste classification, fill level determination,
or cloud computing, neglecting real time identification of
different types of plastics at the disposal site. In this research, a
plastic type recognition model based on edges is developed,
embedded in a smart bin using the Light Weight Convolutional
Neural Network and Tiny ML. The developed recognition model
is able to classify plastic waste into five different types, namely
PET, HDPE, LDPE, PP, and others/mixed. Hence, this result
can be used to automatically sort the plastic waste to its correct
compartment. The model was evaluated using a custom dataset
created by combining images from multiple publicly available
plastic waste image datasets. The lightweight MobileNetV2
architecture based CNN was trained with images of size 160 x
160 pixels (RGB format) and tested against the validation set.
The trained model was then quantized to produce an INT8
TensorFlow Lite model to guarantee a smaller computation
footprint for deploying the model in ESP32 hardware. While the
trained Keras model gave an accuracy score of 92.08% on the
validation data set, the quantized INT8 model produced an
accuracy of 90.68% which translates to an approximate drop in
accuracy of about 1.40 percent point. The implication is that
with the help of lightweight deep learning models and INT8
quantization, one can still achieve an accurate classification of
plastic subtypes. The conclusions drawn from this study is that
TinyML based edge inference can be a practical way for plastic
subtype classification in order to automate the process of smart
waste segregation via classification without relying on cloud
based classification continuously.
