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Automated Robotic Liquid Handling Assembly of Modular DNA Devices

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Automated Quality Control of Cleaning Processes in Automotive Components Using Blob Analysis.

Simone Mari¹, Giovanni Bucci¹, Fabrizio Ciancetta¹

¹Dipartimento di Ingegneria Industriale e Dell'informazione e di Economia, Università dell'Aquila, 67100 L'Aquila, Italy.

Sensors (Basel, Switzerland)

|May 14, 2025

Summary

An automated computer vision system effectively assesses automotive plastic mirror cap cleanliness using adaptive thresholding. This quality control method improves process reliability and reduces waste by detecting surface contaminants.

Keywords:

automotive manufacturing blob analysis computer vision quality control surface inspection

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Area of Science:

Computer Vision
Industrial Quality Control
Surface Metrology

Background:

Automotive plastic mirror caps require pristine surfaces for optimal painting.
Detecting residual contaminants post-washing is critical for quality assurance.
Current inspection methods may lack efficiency and scalability.

Purpose of the Study:

To develop and evaluate an automated computer vision system for assessing plastic mirror cap cleanliness.
To compare the performance of different blob detection algorithms for contaminant identification.
To establish a reliable, cost-effective quality control solution for industrial applications.

Main Methods:

Acquisition of high-resolution monochrome images under varied lighting (natural, IR) and angles.
Implementation and evaluation of adaptive thresholding, LoG, DoG, and DoH blob detection algorithms.
Performance assessment based on detected blob area changes pre- and post-cleaning to determine sensitivity and false positive rates.

Main Results:

Adaptive thresholding under 30° natural light demonstrated superior performance.
This method achieved a statistically significant z-score of +2.05 in the pre-wash phase.
LoG and DoG algorithms showed higher spurious detection rates; DoH performed intermediately but struggled with reflectivity.

Conclusions:

The proposed computer vision system offers a cost-effective and scalable solution for real-time quality control.
Adaptive thresholding provides a reliable method for detecting surface impurities on automotive components.
The system has the potential to enhance process reliability and minimize waste in industrial settings.