Enhancing the Highway Transportation Systems with Traffic Congestion Detection Using the Quadcopters and CNN Architecture Schema-Reference-Cited by-同舟云学术

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Enhancing the Highway Transportation Systems with Traffic Congestion Detection Using the Quadcopters and CNN Architecture Schema

Published:2024 Issue: Volume: Page:247-255
ISSN:2367-4512
Container-title:Lecture Notes on Data Engineering and Communications Technologies
language:en
Short-container-title:

Author:

Kristianto Edy^ORCID,Wiryasaputra Rita^ORCID,Purba Florensa Rosani,Banjarnahor Fernando A.,Huang Chin-Yin^ORCID,Yang Chao-Tung^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-64766-6_24

Reference17 articles.

1. Benjdira, B., Khursheed, T., Koubaa, A., Ammar, A., Ouni, K.: Car detection using unmanned aerial vehicles: comparison between faster R-CNN and YOLOv3. In: 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS), pp. 1–6. IEEE (2019)

2. Bisio, I., Garibotto, C., Haleem, H., Lavagetto, F., Sciarrone, A.: A systematic review of drone based road traffic monitoring system. IEEE Access 10, 101537–101555 (2022)

3. Chakraborty, P., Adu-Gyamfi, Y.O., Poddar, S., Ahsani, V., Sharma, A., Sarkar, S.: Traffic congestion detection from camera images using deep convolution neural networks. Transp. Res. Rec. 2672(45), 222–231 (2018)

4. Chen, C., et al.: YOLO-based UAV technology: a review of the research and its applications. Drones 7(3), 190 (2023)

5. D’Andrea, E., Marcelloni, F.: Detection of traffic congestion and incidents from GPS trace analysis. Expert Syst. Appl. 73, 43–56 (2017)

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