Comparative Analysis of Deep Learning Models for Vehicle Detection

  • Rendi Nurcahyo Gunadarma University
  • Mohammad Iqbal Gunadarma University


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.


Download data is not yet available.


Ammar, Adel., Koubaa1, Anis., Ahmed, Mohanned., Saad, Abdulrahman., 2020. Aerial Images Processing for Car Detection using Convolutional Neural Networks:Comparison between Faster R-CNN and YoloV3.

Chen, L., Ye, Feiyue., Ruan, Y., Fan, Honghui., Chen, Qimei., 2018. An Algorithm for Highway Vehicle Detection Based on Convolutional Neural Network

Deep Learning. [wikipedia] (Updated May 5, 2020) Available at :

Dutta, Suvajit., CS Manideep, Bonthala., Rai, Shalva., V, Vijayarajan.: A Comparative Study of Deep Learning Models for Medical Image Classification. (2017)

Hui, Jonathan., SSD object detection: Single Shot MultiBox Detector for real-time processing. [Medium] (Updated Mar 14, 2018) Available at:

Kathuria, Ayoosh., 2018. What's new in YOLO v3? [Towards Data Science] (Updated Apr 23, 2018) Available at:

Kazakov, I., 2017. Vehicle Detection and Tracking. [Towards Data Science] (Updated May 14, 2017) Available at:

Kumar, Prince., Garg, Vaibhav., Somvanshi, Pavan., Pathanjali.: A Comparative Study of Object Detection Algorithms in A Scene. (2019)

Liu, Wei., Anguelov, Dragomir., Erhan, Dumitru., Szegedy, Christian., Reed, Scott., Fu1, Cheng-Yang., C. Berg, Alexander.: SSD: Single Shot MultiBox Detector. (2016)

Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. (2016)

Redmon, J., Farhadi, Ali.: YOLOv3: An Incremental Improvement. (2018)

Ren, Shaoqing., He, Kaiming., Girshick, Ross., Sun, Jian.: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. (2016)

Shatnawi, Ali., Al-Bdour, Ghadeer., Al-Qurran, Raffi., Al-Ayyoub, Mahmoud.: A Comparative Study of Open Source Deep Learning Frameworks. (2018)

Xiao, Y., 2019. Vehicle Detection in Deep Learning. MSc. Virginia: Virginia Polythecnic Institute and State University

[15] Xu, Joyce., 2017. Deep Learning for Object Detection: A Comprehensive Review. [Towards Data Science] (Updated Sep 12, 2017) Available at:

Xu, Yinghan., Faster R-CNN (object detection) implemented by Keras for custom data from Google’s Open Images Dataset V4. [Towards Data Science] (Updated Nov 20, 2018) Available at :

How to Cite
Nurcahyo, R., & Iqbal, M. (2022). Comparative Analysis of Deep Learning Models for Vehicle Detection. Journal of Systems Engineering and Information Technology (JOSEIT), 1(1), 27 - 32.