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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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A Comprehensive Survey of Machine Learning Techniques and Models for Object Detection.

Maria Trigka1, Elias Dritsas1

  • 1Industrial Systems Institute, Athena Research and Innovation Center, 26504 Patras, Greece.

Sensors (Basel, Switzerland)
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Summary
This summary is machine-generated.

This survey details object detection advancements, highlighting machine learning and deep learning. It covers methodologies, evaluation metrics, and future research for robust computer vision systems.

Keywords:
deep learningmachine learningmodelsobject detectiontechniques

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Area of Science:

  • Computer Vision
  • Artificial Intelligence

Background:

  • Object detection is crucial for AI applications like autonomous driving and medical imaging.
  • Traditional and deep learning (DL) methods have evolved significantly in this field.

Purpose of the Study:

  • To provide a comprehensive analysis of object detection evolution and advancements.
  • To evaluate various methodologies, performance metrics, and challenges in the domain.

Main Methods:

  • Review of traditional and deep learning (DL) object detection techniques.
  • Analysis of performance metrics such as precision, recall, and Intersection over Union (IoU).
  • Examination of challenges including occlusion, scale variation, and real-time processing.

Main Results:

  • Deep learning models, particularly Transformer architectures, show significant progress.
  • Established metrics effectively assess model performance.
  • Key challenges remain in handling occlusions, scale variations, and achieving real-time detection.

Conclusions:

  • Object detection research is rapidly advancing with DL.
  • Addressing current challenges is key to enhancing system robustness, accuracy, and efficiency.
  • Future directions focus on overcoming limitations for broader applications.