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The rapid advancement in computer vision, driven by deep learning, has revolutionised visual data interpretation across various sectors. This study explores object detection models-key to applications like autonomous driving, medical imaging, and surveillance. It reviews and compares SSD, Faster R-CNN, Mask R-CNN, RetinaNet, and YOLOv8, evaluating their architecture, performance on COCO, and limitations. The paper addresses challenges such as diverse environment detection, dataset generalisation, and computational efficiency, suggesting future improvements. The aim is to overview current object detection technologies, highlight challenges, and propose research directions for enhancement.

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Object Detection in Computer Vision: A Comparative Analysis of Advanced Computer Vision Models

  • 会议论文

  • 发表日期：

    2025年

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  • S. Shivakarthik,
  • Krishnanjan Bhattacharjee,
  • Rohan Badiger,
  • Himanshu Bhusan Patro,
  • Swati Mehta

摘要

The rapid advancement in computer vision, driven by deep learning, has revolutionised visual data interpretation across various sectors. This study explores object detection models-key to applications like autonomous driving, medical imaging, and surveillance. It reviews and compares SSD, Faster R-CNN, Mask R-CNN, RetinaNet, and YOLOv8, evaluating their architecture, performance on COCO, and limitations. The paper addresses challenges such as diverse environment detection, dataset generalisation, and computational efficiency, suggesting future improvements. The aim is to overview current object detection technologies, highlight challenges, and propose research directions for enhancement.