.png){kind=icon}
.png){kind=icon}
Return
Recyclable waste image recognition based on deep learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.resconrec.2021.105636.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This study aims to improve the accuracy of waste sorting through deep learning and to provide a possibility for intelligent waste classification based on computer vision/mobile phone terminals. A classification model of recyclable waste images based on deep learning is proposed in this paper. In this waste classification model, the self-monitoring module is added to the residual network model, which can integrate the relevant features of all channel graphs, compress the spatial dimension features, and have a global receptive field. But the number of channels is still kept unchanged; thereby, the model can improve the representation ability of the feature map and can automatically extract the features of different types of waste images. The proposed model was tested on the TrashNet dataset to classify recyclable waste and compare its classification performance with other algorithms. Experimental results show that the image classification accuracy of this model reaches 95.87%.
Keywords:
Image recognition
Recyclable waste classification
Deep learning
Residual network
Self-monitoring module
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
R
IF:
10.9
Papers:
7.1K
Citations:
5.3W