Open AccessDeep transfer learning benchmark for plastic waste classification
Author(s) -
Anthony Ashwin Peter Chazhoor,
Edmond S. L. Ho,
Bin Gao,
Wai Lok Woo
Publication year - 2022
Publication title -
intelligence and robotics
Language(s) - English
Resource type - Journals
ISSN - 2770-3541
DOI - 10.20517/ir.2021.15
Subject(s) - microplastics , plastic pollution , transfer of learning , benchmark (surveying) , convolutional neural network , environmental science , computer science , plastic bag , artificial intelligence , machine learning , sampling (signal processing) , plastic waste , geography , cartography , engineering , ecology , waste management , telecommunications , detector , biology
Millions of people throughout the world have been harmed by plastic pollution. There are microscopic pieces of plastic in the food we eat, the water we drink, and even the air we breathe. Every year, the average human consumes 74,000 microplastics, which has a significant impact on their health. This pollution must be addressed before it has a significant negative influence on the population. This research benchmarks six state-of-the-art convolutional neural network models pre-trained on the ImageNet Dataset. The models Resnet-50, ResNeXt, MobileNet_v2, DenseNet, SchuffleNet and AlexNet were tested and evaluated on the WaDaBa plastic dataset, to classify plastic types based on their resin codes by integrating the power of transfer learning. The accuracy and training time for each model has been compared in this research. Due to the imbalance in the data, the under-sampling approach has been used. The ResNeXt model attains the highest accuracy in fourteen minutes.