Driven by globalization and the rise of e-commerce, the express logistics industry demands fast, safe, and efficient operational processes. Current loading and unloading operation management mainly relies on manual monitoring and experience judgment, facing challenges such as misjudgment, high risk, and inefficiency. Existing image recognition solutions perform poorly in complex warehouse environments, especially in recognizing diverse forklift operation statuses. This dataset aims to improve the accuracy of operational status recognition during forklift loading and unloading processes by addressing visual perception and environmental adaptability issues through large-scale data training. Data collection uses high-resolution industrial cameras, covering different lighting conditions during day and night to ensure comprehensiveness. The data has undergone multiple rounds of quality control, including annotation consistency checks and reviews by professional warehousing and logistics experts. The annotation team comprises more than 50 logistics industry experts and data scientists. Data preprocessing includes image denoising, contrast enhancement, and size normalization. Data is organized and stored in JPG format, with structured file naming and classification labels.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
forklift_type	string	Identifies the model of the forklift appearing in the image.
operator_presence	boolean	Indicates whether an operator is present in the image.
load_type	string	Identifies the type of load carried by the forklift in the image (e.g., boxes, pallets).
operation_status	string	Indicates the current operational status of the forklift in the image (e.g., loading, unloading, idle).
safety_gear	boolean	Determines whether the operator is wearing safety gear (e.g., helmet, reflective vest).
environment_condition	string	Describes the conditions of the environment in which the operation is taking place (e.g., indoor, outdoor, lighting conditions).
collision_risk	boolean	Identifies potential collision risks involving the forklift in the image.

Compliance Statement

Authorization Type	CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
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

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com

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