Scholarlink

<p>Traditional deep learning target detection algorithms face significant accuracy limitations in automated warehousing systems, especially in pallet stacking and occlusion scenarios. Based on this, we propose an innovative transfer learning approach that achieves efficient multi-scale feature extraction and fusion through the improved YOLOv8s-pose architecture and BDEM (boundary detail extraction module)-enhanced FFDPN (feature focus diffusion pyramid). Combined BA Wing loss (boundary-aware Wing loss) and APT-TAL (adaptive power transformation task-aligned labelling) strategy improves detection accuracy at 12 keypoints of pallet E-section. The system achieved 94.4% detection accuracy and 93.2% keypoint positioning accuracy at a processing speed of 104.2 FPS (frames per second). Finally, the LMedS (least median of squares)-based position optimisation solution further improves reliability by controlling the pallet inclination and distance measurement errors to within 4.0° and 29&#xa0;mm, providing a practical solution for automated logistics systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Pallet localization algorithm based on improved human pose estimation with transfer learning

  • 期刊论文

  • 发表日期：

    2025年2月8日

查看全文
  • Zhuguo Zhou,
  • Yujun Lu,
  • Liye Lv

摘要

Traditional deep learning target detection algorithms face significant accuracy limitations in automated warehousing systems, especially in pallet stacking and occlusion scenarios. Based on this, we propose an innovative transfer learning approach that achieves efficient multi-scale feature extraction and fusion through the improved YOLOv8s-pose architecture and BDEM (boundary detail extraction module)-enhanced FFDPN (feature focus diffusion pyramid). Combined BA Wing loss (boundary-aware Wing loss) and APT-TAL (adaptive power transformation task-aligned labelling) strategy improves detection accuracy at 12 keypoints of pallet E-section. The system achieved 94.4% detection accuracy and 93.2% keypoint positioning accuracy at a processing speed of 104.2 FPS (frames per second). Finally, the LMedS (least median of squares)-based position optimisation solution further improves reliability by controlling the pallet inclination and distance measurement errors to within 4.0° and 29 mm, providing a practical solution for automated logistics systems.