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file_name
stringclasses
5 values
quality
stringclasses
3 values
screw_count
stringclasses
3 values
screw_position
stringclasses
5 values
screw_type
stringclasses
3 values
screw_color
stringclasses
3 values
screw_head_type
stringclasses
4 values
anomaly_detected
stringclasses
1 value
3aa33ae6a15174f4930bea3f55d044db.jpg
1280*1672
8
Screws evenly distributed around the motherboard
Phillips screw
Silver
Phillips head
No
42a9cb1d062f0d23833e620530334508.jpg
1280*1706
4
Top and near bottom right corner
Phillips screw
Golden
Phillips
No
446be93b67dfafa214ab1d152aeb2e69.jpg
1280*1672
8
Brass screw holes
Unknown
Brass
Unknown
No
679f3fe7e8cdae234faff5b5156eb9aa.jpg
1280*1706
3
Near the corners of each motherboard
Unknown
Golden
Round hole
No
70225203967d69a3358e6a0ee8bb86d6.jpg
1280*1666
3
Top left, bottom center, top right
Small electronic device screw
Silver
Phillips
No

Mobile Motherboard Screw Detection Dataset

In the current industrial landscape, the quality control of manufacturing processes is critical, especially in electronic assembly. One of the major challenges is ensuring the correct installation of screws on mobile motherboards, where missing, incorrect, or stripped screws can lead to significant product failures. Existing solutions often rely on manual inspection, which is time-consuming and prone to human error. This dataset aims to address the need for automated inspection systems by providing a comprehensive collection of labeled images that reflect various screw conditions. The dataset was collected using high-resolution cameras in a controlled factory environment, ensuring consistent lighting and focus. Quality control measures included multi-round annotations, consistency checks among different annotators, and expert reviews to validate the labels. The images are stored in JPG format and organized into structured directories based on categories of screw status.

Technical Specifications

Field Type Description
file_name string File name
quality string Resolution
screw_count int The actual number of screws detected on the phone motherboard.
screw_position string The detected position information of the screw in the image.
screw_type string The type or specification of the screw.
screw_color string The color of the detected screw.
screw_head_type string The type of screw head, such as Phillips, flathead, etc.
anomaly_detected boolean Whether an anomaly is detected in the image, such as a missing or misaligned screw.

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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