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A Smart Recycling Bin Using Waste Image Classification At The Edge

arXiv 2210.00448 ↗First posted 2 Oct 2022cs.CVcited by 0

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Abstract (arXiv)

Rapid economic growth gives rise to the urgent demand for a more efficient waste recycling system. This work thereby developed an innovative recycling bin that automatically separates urban waste to increase the recycling rate. We collected 1800 recycling waste images and combined them with an existing public dataset to train classification models for two embedded systems, Jetson Nano and K210, targeting different markets. The model reached an accuracy of 95.98% on Jetson Nano and 96.64% on K210. A bin program was designed to collect feedback from users. On Jetson Nano, the overall power consumption of the application was reduced by 30% from the previous work to 4.7 W, while the second system, K210, only needed 0.89 W of power to operate. In summary, our work demonstrated a fully functional prototype of an energy-saving, high-accuracy smart recycling bin, which can be commercialized in the future to improve urban waste recycling.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 42 references that are in the map. First posted on arXiv 2 Oct 2022.

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11 of 42 references.

References · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Densely Connected Convolutional Networkscited by 6,720
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  6. 2019Searching for MobileNetV3cited by 1,994
  7. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,136
  8. 2019Fine-Tuning Models Comparisons on Garbage Classification for Recyclabilitycited by 6
  9. 2020WasteNet: Waste Classification at the Edge for Smart Binscited by 5
  10. 2021Towards artificially intelligent recycling Improving image processing for waste classificationcited by 4
  11. 2021Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysiscited by 1

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