shangzx/Forklift-Loading

---
tags:
- Image Classification
- Object Detection
- Behavior Recognition
- Express Logistics
- Forklift Operations Management
- Warehouse Management
license: cc-by-nc-sa-4.0
task_categories:
- image-classification
language:
- en
pretty_name: Forklift Loading/Unloading Operations Status Detection Image Dataset
size_categories:
- 1B<n<10B
---

# 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.

## 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

<table>
<tr>
<td>Authorization Type</td>
<td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td>
</tr>
<tr>
<td>Commercial Use</td>
<td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td>
</tr>
<tr>
<td>Privacy and Anonymization</td>
<td>No PII, no real company names, simulated scenarios follow industry standards</td>
</tr>
<tr>
<td>Compliance System</td>
<td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td>
</tr>
</table>

## Source & Contact

If you need more dataset details, please visit [Mobiusi](https://www.mobiusi.com/datasets/dee2b81fdd2252c15634a9e88fe78085?utm_source=huggingface&utm_medium=referral). or contact us via [e-mail]

---
tags:
- 图像分类（Image Classification）
- 目标检测（Object Detection）
- 行为识别（Behavior Recognition）
- 快递物流（Express Logistics）
- 叉车作业管理（Forklift Operations Management）
- 仓库管理（Warehouse Management）
license: cc-by-nc-sa-4.0
task_categories:
- 图像分类（image-classification）
language:
- 英语（en）
pretty_name: 叉车装卸作业状态检测图像数据集
size_categories:
- 10亿 < 样本量 < 100亿
---

# 叉车装卸作业状态检测图像数据集

伴随全球化进程加速与电子商务蓬勃兴起，快递物流行业对快速、安全且高效的作业流程提出了迫切需求。当前装卸作业管理多依赖人工监控与经验判断，面临误判率高、作业风险大、效率低下等多重痛点。现有图像识别方案在复杂仓库场景中适配性不佳，尤其难以精准识别多样化的叉车作业状态。本数据集旨在通过大规模数据训练解决视觉感知与环境适配难题，提升叉车装卸作业过程中的作业状态识别准确率。数据采集采用高分辨率工业相机，覆盖昼夜不同光照场景，以保障数据集的全面性与代表性。数据已通过多轮质量管控流程，包括标注一致性校验以及仓储物流领域专家的审核与认证。标注团队由50余名物流行业专家与数据科学家组成。数据预处理环节涵盖图像去噪、对比度增强与尺寸归一化。数据以JPG格式组织存储，采用结构化文件命名规则与分类标签体系。

## 技术规格

| 字段名 | 数据类型 | 字段说明 |
| :--- | :--- | :--- |
| file_name | 字符串 | 文件名称 |
| quality | 字符串 | 图像分辨率 |
| forklift_type | 字符串 | 标识图像中出现的叉车型号 |
| operator_presence | 布尔值 | 指示图像中是否存在作业操作人员 |
| load_type | 字符串 | 标识图像中叉车承载的货物类型（如纸箱、托盘等） |
| operation_status | 字符串 | 指示图像中叉车当前的作业状态（如装载、卸载、待机等） |
| safety_gear | 布尔值 | 判定操作人员是否穿戴安全防护装备（如安全帽、反光背心等） |
| environment_condition | 字符串 | 描述作业开展所在的环境条件（如室内、室外、光照情况等） |
| collision_risk | 布尔值 | 标识图像中叉车是否存在潜在碰撞风险 |

## 合规声明

<table>
<tr>
<td>授权类型</td>
<td>CC-BY-NC-SA 4.0（署名-非商业性使用-相同方式共享）</td>
</tr>
<tr>
<td>商业使用</td>
<td>需获取专属订阅或授权合同（支持按月或按调用量计费）</td>
</tr>
<tr>
<td>隐私与匿名化</td>
<td>不包含个人可识别信息（Personally Identifiable Information，PII），未使用真实企业名称，模拟场景符合行业规范</td>
</tr>
<tr>
<td>合规体系</td>
<td>符合《中华人民共和国数据安全法》、欧盟GDPR，并支持企业级数据访问日志留存</td>
</tr>
</table>

## 来源与联系方式

如需获取更多数据集详情，请访问[Mobiusi](https://www.mobiusi.com/datasets/dee2b81fdd2252c15634a9e88fe78085?utm_source=huggingface&utm_medium=referral)，或通过[e-mail]与我们取得联系。