Logistics Receiving and Sending Area Cargo Stacking Image Dataset
#image classification #object detection #object recognition #express logistics #warehouse management #intelligent sorting system
- Records
- 500 records
- Size
- 1.4G
- Format
- JPG
- Provider
MOBIUSI INC- Updated
- v1.02026-11-04
AI Analysis & Value Prop
In the express logistics industry, the rapid development of e-commerce has led to a surge in cargo flow, posing higher demands on cargo management in logistics receiving and sending areas. However, currently, much cargo stacking and sorting still rely on manual operations, which are prone to errors and inefficiencies. Existing solutions have significant shortcomings in automation and accuracy. This dataset aims to improve the accuracy and efficiency of cargo stacking recognition, meeting the needs for efficient and intelligent operations in the express logistics industry. Data collection is carried out in receiving and sending areas with HD cameras in different logistics centers, taken at various times of the day under different lighting conditions. Quality control involves multiple rounds of labeling and strict consistency checks, reviewed by senior experts in the logistics industry, with a labeling team of 10 experienced professionals. Data preprocessing includes steps such as image enhancement, denoising, and size normalization. The data is stored in JPG format, organized into training, validation, and test sets.
Overview
Dataset Insights
Preview
Sample Examples
d22634c9**.jpg|1920*2560|575.55 KB
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| stacking_type | string | The type of cargo stacking in the logistics receiving and dispatching area. |
| cargo_type | string | The type of cargo stacked in the receiving and dispatching area. |
| stacking_density | float | The density of cargo within the stacking area. |
| obstruction_level | integer | The level of obstruction among cargo, rated from 0 to 10. |
| label_visibility | boolean | Visibility of cargo labels, indicating whether they are easily identifiable. |
| lighting_conditions | string | The lighting conditions during image capture, such as bright, dim, or shadowed. |
| background_clarity | integer | The clarity of the image background, rated from 0 to 10. |
Trust
Compliance Statement
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use
Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization
No PII, no real company names, simulated scenarios follow industry standards
Compliance System
Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs
FAQ
Frequently Asked Questions
- What are the applications for this dataset in express logistics?
- The Logistic Receiving and Dispatch Area Cargo Stacking Image Dataset can be used for optimizing cargo stacking recognition, automated sorting, and improving warehouse management.
- Why is image data important in the logistics industry?
- Image data helps automate cargo recognition and sorting, increases operational efficiency, and reduces human error in the logistics industry.
- How can this dataset improve the accuracy of logistics operations?
- By training computer vision models to enhance recognition of cargo stacking and classification, thereby reducing sorting errors and improving the accuracy of logistics operations.
- Can this dataset be used for machine learning model training?
- Yes, this dataset can be used to train machine learning models for automated cargo stacking recognition and management.
- What advantages does cargo stacking image data provide in express logistics?
- Cargo stacking image data can enhance efficiency in logistics processes, provide more reliable inventory management, and reduce manual intervention.
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Cite this Work
@dataset{Mobiusi2026,
title={Logistics Receiving and Sending Area Cargo Stacking Image Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/9b450f36b079d9c811ac95bfd9f6487c?dataset_scene_id=13},
urldate={2026-02-04},
keywords={cargo stacking image dataset, logistics image recognition, express logistics dataset, image classification dataset},
version={1.0}
}Using this in research? Please cite us.
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