Abstract
Only 4% of household waste generated in Africa is recycled. Current research uses machine learning models in cloud-based solutions to classify waste. However, in countries with limited internet access, there is a need to increase user engagement in classifying waste using an on-device approach. Developing a machine learning model for a mobile device with limited size and speed is a challenge. This research proposes an on-device deep learning framework to encourage the recycling of household waste. The proposed framework combines an optimal deep learning image classification model and gamification elements. A combination of multiple waste datasets named WasteNet consisting of 33,520 images is used to train the deep learning image classification model using seven classes of recyclable waste namely e-waste, garbage, glass, metal, organic, paper and plastic. Data augmentation and transfer learning techniques are applied to train five models on a mobile device namely, MobileNetV2, VGG19, DenseNet201, ResNet152V2 and InceptionResNetV2. Results of the five models are presented in this paper based on accuracy, loss, latency and size. This research shows promise for InceptionResNetV2, MobileNetV2 and DenseNet201in encouraging householders to engage in recycling waste using gamification on a mobile device.
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Ekundayo, O., Murphy, L., Pathak, P., Stynes, P. (2022). An On-Device Deep Learning Framework to Encourage the Recycling of Waste. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-030-82199-9_26
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