Juice Product Occlusion Image Dataset
#object detection #image segmentation #product recognition #image classification #inventory management
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-04-20
AI Analysis & Value Prop
In the current retail e-commerce industry, there is a significant challenge in accurately recognizing products that are partially obscured by packaging or other objects. Existing solutions often struggle with occluded images, resulting in reduced accuracy in product identification and customer dissatisfaction. This dataset aims to address these challenges by providing a diverse collection of images featuring various juice products, such as Tropicana and Minute Maid, under different occlusion scenarios. The data was collected using high-resolution cameras in controlled lighting conditions to ensure clarity and consistency. Rigorous quality control measures were implemented, including multiple rounds of annotation and expert reviews to ensure the reliability of the labels. The dataset is organized in JPG format and consists of structured image files alongside their respective annotations.
Dataset Insights
Sample Examples
d3bc9f83**.png|331*672|357.01 KB
f2008a17**.png|301*677|364.28 KB
0055a1c5**.png|257*641|318.42 KB
35f32cd4**.png|463*680|401.36 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| object_type | string | The type of the main object in the image, such as juice bottle, cup, etc. |
| occlusion_level | string | The level of occlusion of objects in the image, such as no occlusion, partial occlusion, complete occlusion. |
| image_brightness | float | The brightness value of the image, reflecting the overall lighting condition of the image. |
| image_contrast | float | The contrast value of the image, reflecting the level of light and dark contrast in the image. |
| background_clarity | string | The clarity description of the image background, such as blurry or clear. |
| label_visibility | string | The visibility of juice beverage product labels in the image, such as fully visible, partially visible, not visible. |
| dominant_color | string | The most dominant color in the image, such as red, green, blue, etc. |
| label_orientation | string | The orientation of the juice drink label in the image, such as the angle of rotation up, down, left, or right. |
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 |
Frequently Asked Questions
- What types of juice beverages are included in the Juice Beverage Obstruction Recognition Image Dataset?
- This dataset may include various types of juice beverages, such as orange juice, apple juice, grape juice, etc., intended to study recognition performance when these beverages are obstructed.
- How does the Juice Beverage Obstruction Recognition Image Dataset help the retail industry improve recognition accuracy?
- By focusing on the problem of partially obstructed products, this dataset can help machine learning models better recognize and classify juice beverages, thereby improving operational efficiency and accuracy in the retail industry.
- Why is obstruction recognition of juice beverages important?
- Juice beverages on shelves are often obstructed by other products, and accurately recognizing these obstructed items helps with inventory management and the operation of automated checkout systems.
- What machine learning tasks is the Juice Beverage Obstruction Recognition Image Dataset suitable for?
- This dataset is particularly suitable for object detection and image classification tasks, helping models effectively recognize juice beverages under complex conditions.
- What components make up the Juice Beverage Obstruction Recognition Image Dataset?
- The dataset consists of numerous images taken in real retail environments, each containing juice beverage products that are either obstructed or partially obstructed.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Juice Product Occlusion Image Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/43b39a897b303b57aac6210a99eb6821},
urldate={2025-08-28},
keywords={juice product dataset,occlusion image dataset,retail e-commerce images,product recognition dataset},
version={1.0}
}Using this in research? Please cite us.
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