Common garbage classification using mobilenet

• Main Authors: Rabano, Stephenn L., Cabatuan, Melvin K., Sybingco, Edwin, Dadios, Elmer P., Calilung, Edwin J.
• Format: text
• Published: Animo Repository 2018
• Subjects: Refuse and refuse disposal; Androids; Smartphones; Image processing; Samsung Galaxy S6 Edge+; Electrical and Electronics; Systems and Communications
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1716; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2715/type/native/viewcontent
• Institution: De La Salle University

Garbage classification is the first step in waste segregation, recycling, or reuse. MobileNet was used to generate a model that classifies common trash according to the following categories: glass, paper, cardboard, plastic, metal, and other trash. A dataset of 2527 trash images in.jpg extension was used for the training. The model used transfer learning from a model trained on the ImageNet Large Visual Recognition Challenge dataset. The TensorFlow for Poets git repository was cloned as a working directory to retrain the MobileNet model in 500 steps. The resulting baseline model, with a final test accuracy of 87.2% was optimized and quantized. In the Andoid app development, the optimized model (with 89.34% confidence) is preferred over the quantized model (with 1.47% confidence) based on the test using a plastic image. The model app was successfully installed in a Samsung Galaxy S6 Edge}+textbf{{mobile phone. The installed mobile app successfully identified a cardboard material in an image with a}{cardboard container. It is recommended to rerun the training using more steps as this may improve the quantized model performance since a quantized model is fit for mobile devices than models with no quantization. © 2018 IEEE.