Classification of Artificial and Real Objects Using Faster Region-Based Convolutional Neural Networks

Abstract

Object detection and classification tasks can be addressed effectively using machine learning (ML) methods that use convolutional neural networks (CNNs) and region-based convolutional neural networks (R-CNNs). In this study, the ability of R-CNNs to distinguish between digital images of artificial and real objects is evaluated. A single-shot detection (SSD) network is also developed to serve as a baseline approach and for comparative evaluation. Experiments are designed using several images of real and artificial leaves as inputs to the R-CNNs that are trained and tested with different proposal areas of the images. The performances of R-CNNs and SSDs are evaluated using mean average precision (mAP) measure. Results from this study indicate that trained R-CNN s perform well in classification of real and artificial leaves and are robust in performance against changes in many of the experimental factors including minimal training data and resolution of the images. R-CNNs have also performed better than SSDs in the classification tasks with higher values of mAP. The performance of R-CNNs is affected by the proposal area, or the number of subsections the R-CNNs utilizes to determine distinct characteristics of the objects (i.e., leaves) presented. Results based on limited experiments from this study indicate the R-CNNs and their variants are ideally suited for object classification tasks with numerous real-world applications.