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Abstract <p>This paper addresses the problem of object keypoint detection from a single image using modern machine learning methods. Keypoint detection has been extensively studied for human pose estimation, and thus, the study compares deep convolutional neural networks that effectively solve this task. Given the challenge of adapting methods to different object types, special attention is paid to automating the preparation of training data. A novel approach is presented, which includes generating datasets based on 3D models, automatically annotating keypoints, and capturing images of objects from various angles, scales, backgrounds, and lighting conditions. The study investigates which modern deep neural networks are the most effective for keypoint detection and explores the applicability of models trained on synthetic data to real-world scenarios.</p>

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Research on the Solution of the Problem of Detecting Key Points of an Object from a Single Image Using Deep Neural Networks

  • 期刊论文

  • 发表日期：

    2025年9月17日

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  • G. Algashev,
  • V. Kuzina,
  • A. Kupriyanov

摘要

Abstract

This paper addresses the problem of object keypoint detection from a single image using modern machine learning methods. Keypoint detection has been extensively studied for human pose estimation, and thus, the study compares deep convolutional neural networks that effectively solve this task. Given the challenge of adapting methods to different object types, special attention is paid to automating the preparation of training data. A novel approach is presented, which includes generating datasets based on 3D models, automatically annotating keypoints, and capturing images of objects from various angles, scales, backgrounds, and lighting conditions. The study investigates which modern deep neural networks are the most effective for keypoint detection and explores the applicability of models trained on synthetic data to real-world scenarios.