Abstract
The present method of segregating solid waste material is hand picking method and it is prone to dangerous to waste picker’s health because of harmful substances into garbage. So to simplify this process, an automated computer vision-based system will be able to segregate the waste using object detection method. The feature extraction of different solid waste material images is an essential task before object detection. We have used Trashnet dataset along with custom dataset which contains small scale waste materials so as to classify and detect the small scale objects effectively. We have tested and analysed a well-known deep learning models like VGG16, VGG19, ResNet50 and AlexNet, as a result after experimentation we found that VGG16 has 98% training accuracy, 76% test accuracy and data augmentation was applied to increase the accuracy.
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Acknowledgements
This work was shortlisted for the final round of Avishkar Research convention and appreciated this work in Hackathon-2021 on “Recycling Metals, Plastics & Scrap Tyres: Circular Economy Road map to Build a Green India”
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Wadmare, J.G., Patil, S.R. (2022). A Vision-Based Approach for Solid Waste Materials Feature Extraction Using Deep Learning Techniques. In: Bhalla, S., Bedekar, M., Phalnikar, R., Sirsikar, S. (eds) Proceeding of International Conference on Computational Science and Applications . Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-0863-7_9
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DOI: https://doi.org/10.1007/978-981-19-0863-7_9
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