Transfer Learning LinkNet with SegNet for Waste Sorting

To make the cities more sustainable and less damaging to the environment, they should control their rubbish accordingly. Sorting trash into biodegradable and non-biodegradable categories requires a lot of time and effort; otherwise, errors occur frequently when done manually. This research paper provides a hybrid deep learning system which combines Modified LinkNet and Enhanced SegNet and a transfer learning approach to achieve the goal of automated waste classification using high accuracy. The improved SegNet has the ability to clearly differentiate garbage objects on complex backgrounds, unlike the modified LinkNet that uses EfficientNet/MobileNet transfer learning techniques to classify objects. A trained and tested model was trained using over 2,500 labelled images consisting of the TrashNet and TACO datasets. The data was supplemented to strengthen it. Based on the experiment findings, this model is 98 per cent accurate, 97 per cent precise, 96 per cent recall, and 98 per cent specific, which is greater compared to the standard CNN, VGG-16, and ResNet-50 architectures. The analysis conducted by ROC indicated that the AUC equals 0.98, and it is a good indicator that the model is very well able to distinguish between various types of data. The training-validation curves indicated that overfitting was not taking place too much. Being able to leverage the advanced method in relation to smart waste systems that can make the use of rubbish sorting scalable, sustainable, and efficient in real time is a promising idea. The future research will be done on the improvement of categorizing different types of waste and real-time usage optimization on edge devices.