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December 4, 2025Iraqi Journal for Computers and InformaticsOpen Access

Automated Object Detection and Count Estimation Based on Machine Learning Models

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Authors

RJRafil Mohammed Jameel

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Overview

Comparative analysis reveals object detection accuracy varies in real-time applications on edge devices, suggesting model selection must balance speed and precision.

Key Points

  • Real-time applications benefit from efficient object detection, striking a balance between speed and accuracy.
  • Machine learning models, including YOLOv3 and MobileNetv3, were tested against the COCO dataset with varying performance metrics.
  • Evaluation showed that YOLOv3 provides higher confidence scores while MobileNetv3 is optimized for processing speed on edge devices.
  • Selecting the right model for object detection applications is crucial for enhancing performance in diverse fields such as healthcare and automation.

Cite This Study

Rafil Mohammed Jameel (2025) studied this question.

synapsesocial.com/papers/6930dc6bea1aef094cca2007https://doi.org/10.25195/ijci.v51i2.631
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