Abstract: Waste management and recycling is the fundamental part of 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.
Dataset: Dataset of Garbage Images
Original Paper: Classification of Trash for Recyclability Status
♻ Our Paper: https://ieeexplore.ieee.org/document/8466276
Github Repo: Coming soon.
Published: IEEE (SMC) INISTA 2018
Note that this work is part of a collaborative research project by Deep Learning Türkiye, a broad non-profit community dedicated to Deep Learning research in Turkey.
Yazarlar: Cenk Bircanoğlu, Meltem Atay, Fuat Beşer,
Özgün Genç, Merve Ayyüce Kızrak
Dataset: Sign Language Digits Dataset
♻ Our Paper: https://ieeexplore.ieee.org/document/8404385
Note that this work is the first part of a collaborative research project by Deep Learning Türkiye, a broad non-profit community dedicated to Deep Learning research in Turkey.