Object detection using convolutional neural networks

• Main Authors: Galvez, Reagan L., Bandala, Argel A., Dadios, Elmer P., Vicerra, Ryan Rhay P., Maningo, Jose Martin Z.
• Format: text
• Published: Animo Repository 2019
• Subjects: Computer vision; Image processing; Image converters; Neural networks (Computer science); Electrical and Computer Engineering; Electrical and Electronics
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2923
• Institution: De La Salle University

Vision systems are essential in building a mobile robot that will complete a certain task like navigation, surveillance, and explosive ordnance disposal (EOD). This will make the robot controller or the operator aware what is in the environment and perform the next tasks. With the recent advancement in deep neural networks in image processing, classifying and detecting the object accurately is now possible. In this paper, Convolutional Neural Networks (CNN) is used to detect objects in the environment. Two state of the art models are compared for object detection, Single Shot Multi-Box Detector (SSD) with MobileNetV1 and a Faster Region-based Convolutional Neural Network (Faster-RCNN) with InceptionV2. Result shows that one model is ideal for real-time application because of speed and the other can be used for more accurate object detection. © 2018 IEEE.