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Performance Analysis of You Only Look Once, RetinaNet, and Single Shot Detector Applied to Vehicle Detection and Counting

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Information and Communication Technologies (TICEC 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1885))

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Abstract

Artificial intelligence has experienced significant growth in recent decades. As a result, several architectures have been developed for object detection, classification, and recognition. Currently, there are several alternatives that fulfill these purposes; however, there is no rigid framework defining how these architectures are formed. This work presents an updated performance analysis of real-time vehicle detection and counting using You Only Look Once (YOLOv8) version 8, RetinaNet (RN), and Single Shot Detector (SSD). For such analysis, the Google Colaboratory was used as the main retraining environment. The Research-Action methodology was employed to develop the practical case, and a systematic literature review was also conducted to determine the state of the art in this problem domain. For feature extraction, RESNET-50 and MobileNet were used in RN and SSD, respectively. The results indicated that YOLOv8 (which has undergone the most adjustments since its inception) exhibits the best performance in terms of detection time, precision considering the frames that need to be analyzed to enable real-time usage, and ease of implementation.

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Authors and Affiliations

  1. Departamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito, Ecuador

    Iván Andrés Buitrón & Sang Guun Yoo

  2. Smart Lab, Escuela Politécnica Nacional, Quito, Ecuador

    Sang Guun Yoo

Authors
  1. Iván Andrés Buitrón
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  2. Sang Guun Yoo
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Correspondence to Sang Guun Yoo .

Editor information

Editors and Affiliations

  1. Universidad de Cuenca, Cuenca, Ecuador

    Jorge Maldonado-Mahauad

  2. Universidad Laica Eloy Alfaro de Manabí, Manta, Ecuador

    Jorge Herrera-Tapia

  3. Universidad del Azuay, Cuenca, Ecuador

    Jorge Luis Zambrano-Martínez

  4. CEDIA, Cuenca, Ecuador

    Santiago Berrezueta

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Buitrón, I.A., Yoo, S.G. (2023). Performance Analysis of You Only Look Once, RetinaNet, and Single Shot Detector Applied to Vehicle Detection and Counting. In: Maldonado-Mahauad, J., Herrera-Tapia, J., Zambrano-Martínez, J.L., Berrezueta, S. (eds) Information and Communication Technologies. TICEC 2023. Communications in Computer and Information Science, vol 1885. Springer, Cham. https://doi.org/10.1007/978-3-031-45438-7_17

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  • DOI: https://doi.org/10.1007/978-3-031-45438-7_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45437-0

  • Online ISBN: 978-3-031-45438-7

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