Forklift Loading/Unloading Operations Status Detection Image Dataset

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.