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Ultrafast Dynamic Defect Inspection With Computational Neuromorphic Imaging.

Shuo Zhu¹, Qianfeng Yin^2,3, Chutian Wang¹

¹Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong SAR, 999077, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|September 23, 2025

Summary

This summary is machine-generated.

Computational neuromorphic imaging (CNI) offers ultrafast, high-dynamic-range surface defect inspection for manufacturing. This event-based sensor approach overcomes limitations of traditional cameras, enabling real-time diagnosis in challenging industrial settings.

Keywords:

computational neuromorphic imaging defect inspection ultrafast dynamic environments

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Area of Science:

Advanced Manufacturing
Computer Vision
Sensor Technology

Background:

Traditional industrial cameras struggle with latency and limited dynamic range under non-ideal conditions.
Current defect inspection methods are sensitive to vibration and require controlled environments.
Limitations hinder efficiency and quality assurance in high-demand manufacturing.

Purpose of the Study:

To develop a novel inspection paradigm for ultrafast dynamic surface defect detection.
To leverage event-based sensors for enhanced defect inspection capabilities.
To address limitations of traditional imaging in industrial quality control.

Main Methods:

Developed a computational neuromorphic imaging (CNI) approach.
Utilized event-based sensors with high temporal resolution and dynamic range.
Implemented event-driven data processing for real-time analysis and visualization.

Main Results:

Achieved ultrafast inspection with 300-fold sampling time improvement in fast motion.
Demonstrated dynamic ranges exceeding 10,000-fold under varying illumination.
Enabled direct edge detection and visualization of defects using event-driven data.
Showcased enhanced structural defect detection by utilizing vibration.

Conclusions:

CNI provides a cost-effective solution for ultrafast, high-dynamic-range defect inspection.
The approach minimizes perceptual and computational latency for real-time diagnosis.
CNI offers advantages for diverse industrial applications and challenging environments.
This method advances real-time inspection and intelligent diagnosis in precision manufacturing.