AN EFFICIENT CLASSIFICATION OF KITCHEN WASTE USING DEEP LEARNING TECHNIQUES
Main Article Content
Abstract
In the field of environmental protection, recycling of resources and social
livelihoods, wasteclassification was always a crucial subject. A deep learning automated waste
classification approach is introduced to enhance the efficiency of the front-end waste collection.
With the fast increase in global production levels, the problem of garbage disposal is growing
severe. Trash classification is an important step towards waste reduction, harmlessness and
resource utilization. Increasing trash types and quantities implies that traditional scrap
classification algorithms can no longer comply with accurate identification requirements. This
study offers a VGG16 neural network model based on the process of attention for classifying
recyclable waste. The attention module is introduced to the model after convolution so that the
essential information in the feature map may be given greater attention. The algorithm can
automatically extract categorization features such as organic, recyclable and non-recyclable
waste. Experimental findings reveal that 84 per cent of the algorithm in the recyclable trash
classification can effectively categories the garbage.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
