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The huge, intriguing, and diverse range of identifying objects in images falls under computer vision. It is primarily employed in image recovery, observations, safety, etc. The objectives of an object recognition system are to find the object and classify it according to its classification. Inception v2, Tiny YOLO, and SSD Mobile Net are three artificial intelligence object identification algorithms utilizing convolutional neural networks (CNNs), and their outcomes are evaluated. Kairos and Amazon Web Service Recognition (AWS) represent two cloud-based facial verification systems. The outcomes emphasize the constraints of applying such techniques and models in a field like safety, as well as the limitations of utilizing CNN-based techniques to perform visual problems.

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A New Technique for Measuring the Performance on Convolutional Neuronal Networks (CNN) for Object and Image Categorization

  • 章节

  • 发表日期：

    2025年

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  • Siva Skandha Sanagala,
  • Borra Sivaiah,
  • Anuradha Boya,
  • Voruganti Naresh Kumar,
  • V. Ravindernaik,
  • Vankudothu Malsoru

摘要

The huge, intriguing, and diverse range of identifying objects in images falls under computer vision. It is primarily employed in image recovery, observations, safety, etc. The objectives of an object recognition system are to find the object and classify it according to its classification. Inception v2, Tiny YOLO, and SSD Mobile Net are three artificial intelligence object identification algorithms utilizing convolutional neural networks (CNNs), and their outcomes are evaluated. Kairos and Amazon Web Service Recognition (AWS) represent two cloud-based facial verification systems. The outcomes emphasize the constraints of applying such techniques and models in a field like safety, as well as the limitations of utilizing CNN-based techniques to perform visual problems.