Comparison of the YOLOv3 and SSD MobileNet v2 Algorithms for Identifying Objects in Images from an Indoor Robotics Dataset
Adriana Carrillo Rios, Douglas Henke dos Reis, Rodrigo Mattos da Silva, Marco Antônio de Souza Leite Cuadros, Daniel Fernando Tello Gamarra
Abstract
The YOLO and SSD algorithms are tools widely used for detecting objects in images or videos. This is due to the speed of detection and good performance in the identification of objects. This article presents a comparison of the YOLOv3 and SSD MobileNet v2 algorithms for identifying objects in images through simulations, the dataset used is an indoor robotics dataset. In order to reach the objective, several training sessions were carried out to analyze the behavior of each model when detecting objects in images. After analyzing the results, a better performance of the YOLOv3 model was observed, although this model takes more time to complete the training for the same number of steps compared to the SSD MobileNet v2 model. It is worth mentioning that this work presents for the first time a comparison between the SSD MobileNet v2 and YOLOv3 algorithms.