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Comparative Evaluation of Convolutional Neural Network Object Detection Algorithms for Vehicle Detection.

Saieshan Reddy1, Nelendran Pillay1, Navin Singh1

  • 1Department of Electronic and Computer Engineering, Durban University of Technology, Durban 4001, South Africa.

Journal of Imaging
|July 26, 2024
PubMed
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Single Shot MultiBox Detector (SSD) excels in vehicle detection using Convolutional Neural Networks (CNNs), offering superior speed and accuracy over Faster Region-Based Convolutional Network (R-CNN) and You Only Look Once v3 (YOLO). This CNN-based approach achieves the highest mean average precision for efficient object detection.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning

Background:

  • Convolutional Neural Networks (CNNs) have transformed object detection.
  • Vehicle detection is a critical application within computer vision.

Purpose of the Study:

  • To compare the performance of Faster Region-Based Convolutional Network (R-CNN), You Only Look Once v3 (YOLO), and Single Shot MultiBox Detector (SSD) for vehicle detection.
  • To analyze architectural intricacies, methodological differences, and performance metrics of these CNN-based algorithms.

Main Methods:

  • Evaluation of Faster R-CNN, YOLO v3, and SSD algorithms on a vehicle detection task.
  • Comparative analysis of detection speed, mean average precision (mAP), and average loss.

Main Results:

Keywords:
Convolutional Neural NetworksFaster R-CNNMATLABObject DetectionSSDYOLO

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  • Single Shot MultiBox Detector (SSD) achieved the highest mAP (0.92) at the fastest average speed (0.5s).
  • Faster R-CNN had an mAP of 0.76 (5.1s), while YOLO v3 achieved an mAP of 0.81 (1.16s).
  • All three detectors exceeded 99% accuracy, with SSD showing a higher average loss (2.625).

Conclusions:

  • Single Shot MultiBox Detector (SSD) demonstrates superior performance for vehicle detection compared to Faster R-CNN and YOLO v3.
  • SSD offers a compelling balance of high accuracy and processing speed for real-time object detection applications.