Efficient vehicle detection and classification algorithm using faster R-CNN models

• Autorzy: Mallahi Imad El , Riffi Jamal , Tairi Hamid , Mahraz Mohamed Adnane

Identyfikatory

DOI

10.14313/JAMRIS/4‐2024/33

• Języki publikacji: EN

Abstrakty

EN

This study proposes an integrated framework for efficient traffic object detection and classification by leveraging advanced deep-learning techniques. The framework begins with the input of video surveillance, followed by an image-acquisition process to extract the relevant frames. Subsequently, a Faster R-CNN (ResNet-152) architecture was employed for precise object detection within the extracted frames. The detected objects are then classified using deep reinforcement learning, specifically trained to identify distinct traffic entities, such as buses, cars, trams, trolleybuses, and vans. The UA-DETRAC dataset served as the primary data source for training and eval- uation, ensuring the model’s adaptability to real-world traffic scenarios. Finally, the performance of the framework was assessed using key metrics, including precision, recall, and F1 score, providing insights into its effectiveness in accurately detecting and classifying traffic objects. This integrated approach offers a promising solution to enhance traffic surveillance systems and facilitate improved traffic management and safety measures in urban environments.

Słowa kluczowe

EN

classification; deep learning; vehicle detection; video surveillance; traffic estimation; efficient R-CNN; traffic detection; convolutional neural network

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2024
• Tom: Vol. 18, No. 4
• Strony: 86--93
• Opis fizyczny: Biblioigr. 39 poz., rys.

Twórcy

autor

Mallahi Imad El

• Sidi Mohamed Ben Abdellah University, Faculty of Sciences, Department of Computer Sciences, Fez, Morocco

• https://orcid.org/0000-0001-9735-6799+

autor

Riffi Jamal

• Sidi Mohamed Ben Abdellah University, Faculty of Sciences, Department of Computer Sciences, Fez, Morocco

• https://orcid.org/0000-0003-0818-7706

autor

Tairi Hamid

• Sidi Mohamed Ben Abdellah University, Faculty of Sciences, Department of Computer Sciences, Fez, Morocco

• https://orcid.org/0000-0002-5445-0037

autor

Mahraz Mohamed Adnane

• Sidi Mohamed Ben Abdellah University, Faculty of Sciences, Department of Computer Sciences, Fez, Morocco

• https://orcid.org/0000-0002-0966-9654

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).