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Page 2 Chazhoor et al. Intell Robot 2022;2:1-19 https://dx.doi.org/10.20517/ir.2021.15

[3]
exploration gives enormous leverage over heavy and expensive alternatives . The packaging industry widely
uses plastics after the e-commerce revolution because they are lightweight, cheap, and abundant. In 2015,
the packing sector produced 141 million metric tons of garbage, accounting for 97 percent of all waste
[4]
produced concerning the total consumption in the packaging sector . Discarded polyethylene terephthalate
(PETE) bottles are a common source of household waste. In 2021, global waste plastic bottle consumption
[2]
will surpass 500 billion as estimated .
The increasing use of plastics and their wastage negatively affect the global economy. This surge in
consumption and the low degradability of plastic have resulted in massive plastic accumulation in the
environment, which has harmed ecosystems and human health . This has resulted in countries formulating
[5]
strict policies for plastics and even banning some types of single-use plastics. Plastics are non-biodegradable
and considerably take a longer time to degrade. Reusing and recycling are viable ways to stop contaminating
the environment with plastic pollution . Plastic wastes can be retrieved after entering the municipal
[6]
treatment plants or before it. However, the plastic waste from the municipal treatment plants is usually
contaminated and ends up in landfills or incineration centers. The plastic waste collected outside of such
plants is relatively cleaner and can be reused or recycled. Recovered plastics from such wastes have varied
types of plastic, making it extremely difficult to identify and sort different kinds of plastics.

By integrating transfer learning, the Dataset needs only a limited number of input images to acquire high
accuracy, and it also accelerates the training of neural networks, consequently improving the classification
of multiple classes in a dataset . Balancing the number of images in each class compensates for the class
[7]
imbalance problem. This research contributes towards benchmarking of pre-trained models and concluding
that the ResNeXt model achieves the highest accuracy on the WaDaBa dataset from the list of pre-trained
models specified in this paper.

1.1. Literature review
Seven different varieties of plastics exist in the modern day. They are classified as Polyethylene terephthalate
(PET or PETE), high-density polyethylene (HDPE), polyvinyl chloride (PVC or Vinyl), low-density
polyethylene (LDPE), polypropylene (PP), polystyrene (PS or Styrofoam) and Others, which does not
belong to any of the above types, has been shown in Figure 1 .
[3]

1.1.1. Traditional sorting techniques
Initially, segregation of wastes and separation of different types of plastics were done manually. However,
[6]
this results in increased labor costs and time consumption . Traditional macro sorting of plastics was
performed with the aid of sensors which included near-infrared spectrometers , x-ray transmission sensor,
[8,9]
Fourier transformed Infrared Technique , laser aided identification, and marker identification by
[10]
identifying the resin type . However, these approaches are limited to recognizing just particular types of
[11]
plastics and are costly due to the large equipment required. The intricacy of mechanical sorting and its
maintenance, as well as the high initial investment, are the drawbacks of traditional sorting methods.

1.1.2. Modern sorting techniques
Deep learning has made classification easier, more efficient, and cost-effective, with less human
intervention. The deep learning approach was enhanced by convolutional neural networks (CNN) . CNNs
[12]
are excellent for object classification and detection . After the model has been trained on the data, the
[13]
plastics may be sorted into the appropriate classes with the assistance of CNN. They do, however, require a
huge quantity of training data, which might be difficult to get at times. When the input data is small, the
problem of overfitting develops, resulting in inaccurate classifications . Transfer learning reduces the
[14]