2019/08/07 by Umut Özkaya, Umut Ozkaya, Ozkaya, Umut +2 · 1 citation
Computer Science · Engineering · Environmental Science · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Municipal Solid Waste Management #Vehicle License Plate Recognition #cs.CV
paper · pdf · doi:10.48550/arxiv.1908.04393
published in ISAS 2018-Winter
arxiv created 2019/08/07 · openalex publication_date 2019/08/07 · arxiv updated 2019/08/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this study, it is aimed to develop a deep learning application which detects types of garbage into trash in order to provide recyclability with vision system. Training and testing will be performed with image data consisting of several classes on different garbage types. The data set used during training and testing will be generated from original frames taken from garbage images. The data set used for deep learning structures has a total of 2527 images with 6 different classes. Half of these images in the data set were used for training process and remaining part were used for testing procedure. Also, transfer learning was used to obtain shorter training and test procedures with and higher accuracy. As fine-tuned models, Alexnet, VGG16, Googlenet and Resnet structures were carried. In order to test performance of classifiers, two different classifiers are used as Softmax and Support Vector Machines. 6 different type of trash images were correctly classified the highest accuracy with GoogleNet+SVM as 97.86%.