Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO

Abstract

Due to its numerous applications and new technological advancements, object detection has gained more attention in the last few years. This study examined various uses of some deep learning object detection algorithms. These algorithms are divided into two-stage detectors like Region Based Convolutional Neural Network (R-CNN), Fast Region Based Convolutional Neural Network (Faster R-CNN), and Faster Region Based Convolutional Neural Network (Faster R-CNN), and one-stage detectors like Single Shot MultiBox Detector (SSD) and You Only Look Once (YOLO) algorithms that are used in text and face detection, image retrieval, security, surveillance, traffic control, traffic sign/light detection, pedestrian detection and in medical areas among others. This research primarily focuses on three applications: drone surveillance, applications relating to traffic, and medical fields. From the analysis performed, it was found that of the various deep learning models used in the various application areas, YOLO, a one-stage detector, is the most popular algorithm for drone surveillance. SSD, another one-stage detector, was primarily used in applications related to traffic, and Faster R-CNN, a two-stage detector, is the most popular algorithm for applications in the medical field