Performance Comparison of Faster R-CNN and SSD in Vehicle Detection from Aerial Traffic Videos

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Tarih

2024

Yazarlar

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Institute of Electrical and Electronics Engineers Inc.

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Vehicle detection is essential for urban planning, traffic management, and various security and surveillance applications. This study compares the performance of two popular deep learning algorithms, Faster R-CNN and SSD, for vehicle detection using traffic video recordings obtained from unmanned aerial vehicles. The dataset, comprising images taken from diverse angles, heights, and lighting conditions, was utilized to assess the effectiveness of both models. Faster R-CNN demonstrated superior accuracy in detecting vehicles and motorcycles under various conditions, including night scenarios and scenes with obstacles such as shadows, traffic signs, and light poles. However, it struggled with detecting very small objects and those with color tones similar to the background. In contrast, while SSD performed well under optimal lighting, it exhibited limitations in detecting smaller vehicles partially obscured by environmental elements. Quantitative analysis revealed that Faster R-CNN had 13.21% higher precision, 12.5% higher recall, and 10.04% higher mAP compared to SSD. Despite its superior detection performance, Faster R-CNN's longer detection time indicates that SSD may be more suitable for real-time applications where speed is crucial. © 2024 IEEE.

Açıklama

IEEE SMC; IEEE Turkiye Section
2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 16 October 2024 through 18 October 2024 -- Ankara -- 204562

Anahtar Kelimeler

automated traffic management, Faster R-CNN, SSD, vehicle detection

Kaynak

2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

WoS Q Değeri

Scopus Q Değeri

N/A

Cilt

Sayı

Künye

Bağlantı

https://doi.org/10.1109/ASYU62119.2024.10757013
 https://hdl.handle.net/20.500.13099/1386

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu