E-ISSN 3026-930X
 

Review Article 


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

Oluwaseyi Ezekiel Olorunshola, Paul Olugbeji Jemitola, Adeniran Kolade Ademuwagun.


Cited By:32

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

Key words: R-CNN, Fast R-CNN, Faster R-CNN, SSD, YOLO, Object Tracking.


 
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How to Cite this Article
Pubmed Style

Olorunshola OE, Jemitola PO, Ademuwagun AK. Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO. NJEAS. 2023; 1(1): -. doi:10.5455/NJEAS.150264


Web Style

Olorunshola OE, Jemitola PO, Ademuwagun AK. Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO. https://www.nilejeas.com/?mno=150264 [Access: June 27, 2026]. doi:10.5455/NJEAS.150264


AMA (American Medical Association) Style

Olorunshola OE, Jemitola PO, Ademuwagun AK. Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO. NJEAS. 2023; 1(1): -. doi:10.5455/NJEAS.150264



Vancouver/ICMJE Style

Olorunshola OE, Jemitola PO, Ademuwagun AK. Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO. NJEAS. (2023), [cited June 27, 2026]; 1(1): -. doi:10.5455/NJEAS.150264



Harvard Style

Olorunshola, O. E., Jemitola, . P. O. & Ademuwagun, . A. K. (2023) Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO. NJEAS, 1 (1), -. doi:10.5455/NJEAS.150264



Turabian Style

Olorunshola, Oluwaseyi Ezekiel, Paul Olugbeji Jemitola, and Adeniran Kolade Ademuwagun. 2023. Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO. Nile Journal of Engineering and Applied Science, 1 (1), -. doi:10.5455/NJEAS.150264



Chicago Style

Olorunshola, Oluwaseyi Ezekiel, Paul Olugbeji Jemitola, and Adeniran Kolade Ademuwagun. "Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO." Nile Journal of Engineering and Applied Science 1 (2023), -. doi:10.5455/NJEAS.150264



MLA (The Modern Language Association) Style

Olorunshola, Oluwaseyi Ezekiel, Paul Olugbeji Jemitola, and Adeniran Kolade Ademuwagun. "Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO." Nile Journal of Engineering and Applied Science 1.1 (2023), -. Print. doi:10.5455/NJEAS.150264



APA (American Psychological Association) Style

Olorunshola, O. E., Jemitola, . P. O. & Ademuwagun, . A. K. (2023) Comparative Study of Some Deep Learning Object Detection Algorithms: R-CNN, FAST R-CNN, FASTER R-CNN, SSD, and YOLO. Nile Journal of Engineering and Applied Science, 1 (1), -. doi:10.5455/NJEAS.150264