Image Classification Approach Using Machine Learning and an Industrial Hadoop Based Data Pipeline

Abstract

In smart cities, citizens contribute to improving the overall quality of life through infrastructure deficiency signaling. Multimedia content (images, videos) uploaded using smartphones allow city authorities to take appropriate incident responses. This paper proposes a benchmark of machine learning (ML) algorithms for image classification, evaluated on a small dataset of images captured by citizens that cover problems related to water and electricity distribution. The final goal is to label each image into its corresponding class to take the appropriate decisions to tackle the reported problem. A number of classical supervised ML algorithms along with deep learning methods are trained and compared. The experimental results demonstrate that transfer learning with data augmentation and fine-tuning using VGG16 network achieves high classification precision and a desirable time performance. We also deployed our models through a Hadoop based data pipeline which led to a significant enhancement in the precision and the image classification time.