The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Application of deep learning object classifier to improve e-waste collection planning
Open accessAbstract
This study investigates an image recognition system for the identification and classification of waste electrical and electronic equipment from photos. Its main purpose is to facilitate information exchange regarding the waste to be collected from individuals or from waste collection points, thereby exploiting the wide acceptance and use of smartphones. To improve waste collection planning, individuals would photograph the waste item and upload the image to the waste collection company server, where it would be recognized and classified automatically. The proposed system can be operated on a server or through a mobile app. A novel method of classification and identification using neural networks is proposed for image analysis: a deep learning convolutional neural network (CNN) was applied to classify the type of e-waste, and a faster region-based convolutional neural network (R-CNN) was used to detect the category and size of the waste equipment in the images. The recognition and classification accuracy of the selected e-waste categories ranged from 90 to 97%. After the size and category of the waste is automatically recognized and classified from the uploaded images, e-waste collection companies can prepare a collection plan by assigning a sufficient number of vehicles and payload capacity for a specific e-waste project.
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Cites
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Cited by
A smart municipal waste management system based on deep-learning and Internet of Things
Wang, C., Qin, J., Qu, C., Ran, X., Liu, C., & Chen, B. (2021) · Waste Management · doi:10.1016/j.wasman.2021.08.028 · Cited by 259
Sallang, N. C. A., Islam, M. T., Islam, M. S., & Arshad, H. (2021) · IEEE Access · doi:10.1109/access.2021.3128314 · Cited by 131
Development of an automatic sorting robot for construction and demolition waste
Xiao, W., Yang, J., Fang, H., Zhuang, J., Ku, Y., & Zhang, X. (2020) · Clean Technologies and Environmental Policy · doi:10.1007/s10098-020-01922-y · Cited by 72
Metadata
- Authors
- Nowakowski, P., & Pamuła, T.
- Venue
- Waste Management
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 109
- Pages
- 1-9
- ISSN
- 0956-053X (ISSN-L)
- OpenAlex
- W3022493227
- Cited by
- 259
- References
- 36
- FWCI
- 12.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
