Datasets:
file_name stringclasses 5 values | quality stringclasses 5 values | socket_type stringclasses 5 values | copper_tab_presence stringclasses 3 values | conductance_status stringclasses 5 values | defect_type stringclasses 5 values | inspection_angle stringclasses 5 values | copper_tab_position stringclasses 5 values | anomalies_flag stringclasses 5 values |
|---|---|---|---|---|---|---|---|---|
27692833890721ebfeef6448abb158cd.png | 1268*1500 | Wall-Embedded Socket | Copper Tab Not Detected | Non-Conductive | Defect Type Not Detected | Straight View | Not Located | Marked as Anomaly |
99627a18676ca55ebc07f32573b8aa4d.png | 1128*1500 | Wall Socket | Copper Tab Not Detected | Conductance Unknown | No Visible Defect | Front View | Copper Tab Position Unknown | No Anomalies Flagged |
c42b150ac2f41f6d6aed4d6639497fcb.png | 1117*1500 | Multiple Power Socket | Copper Tab Detected | Conductivity Normal | No Obvious Defect | Frontal View | Installation Position Normal | No Anomaly Flagged |
cd0bdc241bca019b0ef29500201a59e3.png | 1535*1500 | T-type socket | copper detected | undetected | no apparent defect | top-down | correct | no |
dc15d914227a75e5d5c4b37c03ef8104.png | 1509*1500 | Unknown | Copper Tab Detected | Unknown | Exposed Conductors | Shot from Left Side | Central Area | Yes |
Power Socket Copper Plate Installation Detection Dataset
In the industrial sector, the quality of power socket assembly is critical to ensure safety and functionality. However, there are significant challenges such as inconsistent installation and human error, leading to potential failures in electrical systems. Existing solutions often rely on manual inspection, which can be time-consuming and error-prone. This dataset aims to address these issues by providing a comprehensive resource for training machine learning models that can automatically detect whether conductive components are installed correctly, thereby improving efficiency and reliability. The dataset consists of 5000 images captured under controlled lighting conditions using high-resolution cameras, focusing on various angles of power sockets. Quality control measures include multi-round annotation, consistency checks among annotators, and expert reviews to ensure high annotation precision. The data is stored in JPG format, organized in a directory structure that allows for easy access and retrieval of images based on their installation status.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| socket_type | string | The specific model or type of power socket. |
| copper_tab_presence | boolean | Whether a copper clip is detected in the power socket. |
| conductance_status | boolean | The detection result of whether the power socket is conductive. |
| defect_type | string | Detected types of socket defects, such as missing copper pieces, misalignment, etc. |
| inspection_angle | string | The angle or perspective of the camera when capturing the socket image. |
| copper_tab_position | string | The specific position of the copper piece in the socket, used for precise location detection. |
| anomalies_flag | boolean | Whether the image is marked as having anomalies or potential installation issues. |
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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