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A New Pallet-Positioning Method Based on a Lightweight Component Segmentation Network for AGV Toward Intelligent

Bin Wu1, Shijie Wang1, Yi Lu1

  • 1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new lightweight network for precise pallet segmentation and localization in warehouses, improving automated guided vehicle (AGV) operations. The method enhances accuracy by over 10% and processing speed by 32%, boosting warehouse efficiency.

Keywords:
attention mechanismcomponent segmentationdeep learninggeneralization capabilitypallet positioning

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Automated Guided Vehicles (AGVs) face challenges in warehouses due to varied pallet sizes, hindering operational efficiency.
  • Existing semantic segmentation models struggle to balance spatial details and high-level semantic information, leading to redundant computations.

Purpose of the Study:

  • To propose a lightweight component segmentation network for precise pallet segmentation and localization.
  • To address the limitations of existing models in handling diverse pallet shapes and sizes for automated picking.

Main Methods:

  • A novel lightweight component segmentation network with a dual-attention mechanism and an encoder-decoder architecture.
  • Integration of a residual structure to reduce network parameters and mitigate gradient issues.
  • Utilizing dual-branch input images to extract multi-scale features for enhanced segmentation.

Main Results:

  • Achieved precise segmentation of various pallet types using limited annotated images.
  • Demonstrated robustness in pallet localization under varying illumination and background noise.
  • Improved accuracy by 10.41% and image processing speed by 32.8% compared to traditional models.

Conclusions:

  • The proposed network effectively segments and localizes multi-category pallets in complex warehousing environments.
  • The method enhances AGV operational efficiency through accurate pallet identification and positioning.
  • Validated robustness and performance in real-world warehousing scenarios with diverse conditions.