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Mohammad Iqbal
Gunadarma University
Computer Science & IT

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Comparative Analysis of Deep Learning Models for Vehicle Detection Rendi Nurcahyo; Mohammad Iqbal
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 1 (2022): March 2022
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (610.663 KB) | DOI: 10.29207/joseit.v1i1.1960

Abstract

Deep Learning techniques are now widely used instead of traditional Computer Vision. There are many Deep Learning model algorithms for each use case such as Object Detection has several models, including Faster R-CNN, SSD, and YOLO v3. The performance and results of each Deep Learning model have advantages and disadvantages. Therefore, we must determine which model is suitable for the use cases and datasets that we have so that we can make the best Deep Learning model. Based on this need, this paper will make a comparative analysis of the Deep Learning model for Vehicle Detection (the spesific of Object Detection) from the models mentioned, namely, Faster R-CNN, SSD (Single Shot Detector), and YOLO v3 (You Only Look Once) to see the advantages and the disadvantages and which ones are the best. And after a comparison, it was concluded that of the three models mentioned only YOLO v3 model is able to be used as real time detection because it has low latency due to YOLO v3 only performs single convolution process so that it makes the process simpler and faster without reduce the accuracy.
Co-Authors Rendi Nurcahyo