Suction Hook Slot Installation Detection Dataset
#object detection #image recognition #machine learning #automated assembly #retail management #product recognition
- 15000 records
- 2.9G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
The current retail e-commerce industry is facing issues of low efficiency in automated assembly. Traditional assembly methods rely on manual labor, which is prone to errors and is not very efficient. Existing solutions often depend on simple image recognition technology, which cannot meet the precise detection needs in complex environments. This dataset aims to solve slot detection issues in the automated assembly process by providing high-quality object detection data, thus improving overall assembly efficiency. The dataset construction involves acquiring images with various devices in different environments to ensure data diversity and representativeness. We implemented multiple rounds of annotation and consistency checking mechanisms to ensure high data quality, and conducted expert reviews. The data is stored in JPG format, organized by image ID for easy invocation and analysis.
Dataset Insights
Sample Examples
7a431774**.jpg|1224*1632|112.65 KB
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d28fbaba**.jpg|1224*1632|71.36 KB
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| slot_presence | boolean | Indicates whether a suction cup hook installation slot is present in the image. |
| slot_position | string | Describes the general location of the suction cup hook installation slot in the image, such as top left, bottom right, etc. |
| slot_shape | string | Defines the shape of the suction cup hook installation slot, such as circular, rectangular, etc. |
| slot_size | string | Measures the size of the suction cup hook installation slot to determine suitability for specific hooks. |
| slot_orientation | string | Records the orientation of the suction cup hook installation slot, such as vertical, horizontal, etc. |
| slot_material | string | Describes the material of the suction cup hook installation slot, such as plastic, metal, etc. |
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 |
Frequently Asked Questions
- What information does the Suction Cup Hook Installation Slot Detection Dataset contain?
- This dataset contains image data for recognizing and detecting suction cup hook installation slots, with annotations specifying the detection targets.
- Which industry is this dataset suitable for?
- The Suction Cup Hook Installation Slot Detection Dataset is suitable for the retail industry, especially in product assembly involving suction cup hooks.
- How to use this dataset for object detection?
- Deep learning object detection algorithms like YOLO or Faster R-CNN can be used to train on this dataset to automatically recognize and detect the installation positions for suction cup hooks.
- How does this dataset assist in automation assembly?
- By enhancing the detection accuracy of suction cup hook installation slots, this dataset supports automated systems to perform rapid and precise assembly, thus improving production efficiency.
- What technical background is needed to use this dataset?
- Using this dataset for object detection typically requires a foundational knowledge of computer vision and deep learning, and familiarity with frameworks like TensorFlow or PyTorch.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Suction Hook Slot Installation Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/f5f656b4dfcb656fb1e34a15d8f683bd?dataset_scene_id=9},
urldate={2025-09-15},
keywords={object detection dataset, retail e-commerce dataset, automated assembly, image recognition},
version={1.0}
}Using this in research? Please cite us.
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