Publication: Recyclenet: intelligent waste sorting using deep neural networks
Program
KU-Authors
KU Authors
Co-Authors
Bircanoglu, Cenk
Atay, Meltem
Beser, Fuat
Kizrak, Merve Ayyuce
Advisor
Publication Date
Language
English
Journal Title
Journal ISSN
Volume Title
Abstract
Waste management and recycling is the fundamental part a sustainable economy. For more efficient and safe recycling, it is necessary to use intelligent systems instead of employing humans as workers in the dump-yards. This is one of the early works demonstrating the efficiency of latest intelligent approaches. In order to provide the most efficient approach, we experimented on well-known deep convolutional neural network architectures. For training without any pre-trained weights, Inception-Resnet, Inception-v4 outperformed all others with 90% test accuracy. For transfer learning and fine-tuning of weight parameters using ImageNet, DenseNet121 gave the best result with 95% test accuracy. One disadvantage of these networks, however, is that they are slightly slower in prediction time. To enhance the prediction performance of the models we altered the connection patterns of the skip connections inside dense blocks. Our model RecycleNet is carefully optimized deep convolutional neural network architecture for classification of selected recyclable object classes. This novel model reduced the number of parameters in a 121 layered network from 7 million to about 3 million.
Description
Source:
2018 Innovations In Intelligent Systems and Applications (Inista)
Publisher:
IEEE
Keywords:
Subject
Computer science, Artificial intelligence, Theory methods, Engineering, Electrical electronic engineering