AN EFFICIENT CLASSIFICATION OF KITCHEN WASTE USING DEEP LEARNING TECHNIQUES (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
In the field of environmental protection, recycling of resources and sociallivelihoods, wasteclassification was always a crucial subject. A deep learning automated wasteclassification 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 growingsevere. Trash classification is an important step towards waste reduction, harmlessness andresource utilization. Increasing trash types and quantities implies that traditional scrapclassification algorithms can no longer comply with accurate identification requirements. Thisstudy offers a VGG16 neural network model based on the process of attention for classifyingrecyclable waste. The attention module is introduced to the model after convolution so that theessential information in the feature map may be given greater attention. The algorithm canautomatically extract categorization features such as organic, recyclable and non-recyclablewaste. Experimental findings reveal that 84 per cent of the algorithm in the recyclable trashclassification can effectively categories the garbage.