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A Vision-Based Method for Detecting the Position of Stacked Goods in Automated Storage and Retrieval Systems.

Chuanjun Chen^1,2, Junjie Liu², Haonan Yin¹

¹Department of Automation, Tsinghua University, Beijing 100084, China.

Sensors (Basel, Switzerland)

|April 26, 2025

Summary

This study introduces STEGNet, a new machine vision algorithm for monitoring cargo in automated storage and retrieval systems (AS/RS). It improves detection accuracy and efficiency for better logistics operations.

Keywords:

automated storage and retrieval systems computer vision object detection object segmentation

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Area of Science:

Computer Vision
Machine Learning
Logistics Technology

Background:

Automated storage and retrieval systems (AS/RS) are vital in modern logistics.
Monitoring cargo stacking patterns in AS/RS is a significant challenge.
Existing computer vision and deep learning methods face limitations in accuracy, efficiency, and adaptability.

Purpose of the Study:

To develop a novel machine vision-based detection algorithm for automated cargo stack monitoring.
To enhance the accuracy and efficiency of cargo detection and pose estimation in AS/RS.
To provide a robust solution for complex warehouse environments.

Main Methods:

Integration of a pallet surface object detection network (STEGNet) with a box edge detection algorithm.
STEGNet utilizes an Efficient Gated Pyramid Feature Network (EG-FPN) with Gated Feature Fusion and a Lightweight Attention Mechanism.
Incorporation of a geometric constraint method for box edge detection and a Perspective-n-Point (PnP) approach for 2D-to-3D pose estimation.

Main Results:

STEGNet achieved 93.49% mAP on the GY-WSBW-4D dataset and 83.2% mAP on the WSGID-B dataset.
A lightweight STEGNet variant reduced model size by 34% and increased inference speed by 68% with competitive accuracy.
Practical application demonstrated pose estimation with Mean Absolute Error < 4 cm and Rotation Angle Error < 2°.

Conclusions:

The proposed machine vision algorithm offers a reliable and efficient solution for automated cargo stack monitoring in AS/RS.
STEGNet demonstrates superior performance compared to existing benchmarks in detection accuracy and computational efficiency.
The system's robust performance in complex environments addresses key challenges in modern logistics operations.