What challenges are currently facing machine vision defect detection?

Machine vision defect detection is a key technology in the field of industrial automation, capable of significantly enhancing production efficiency and product quality.  However, in practical applications, machine vision defect detection faces numerous challenges.  The following provides a detailed analysis of these challenges:

Large data volume and slow processing speed: In actual production lines, the volume of image data that needs to be detected is often enormous, posing extremely high demands on processing speed. Traditional machine vision algorithms may encounter performance bottlenecks when dealing with large amounts of data, leading to reduced detection speed and affecting the efficiency of the production line.

Diversity of defect types: Different products may exhibit a wide range of defects with extremely complex types. For instance, surface scratches, stains, uneven color, deformation, etc., all pose significant challenges to defect detection.  Designing a universal detection system capable of accurately identifying various defects is a major challenge in the field of machine vision.

Impact of lighting and environmental changes: In actual production environments, lighting conditions and environmental factors are often uncertain, which can directly affect the quality of collected images, thereby affecting the accuracy of defect detection. For example, strong light or shadows may cause certain areas to be overexposed or underexposed in the image, making it impossible to identify defects within them.

Background noise and interference factors: Background noise on the production line, mutual obstruction between products, and various interference factors such as dust and water droplets may all interfere with defect detection. How to eliminate these interferences and improve the robustness of detection is another challenge faced by machine vision defect detection.

Scalability and adaptability of algorithms: As production lines are upgraded or products change, defect detection algorithms need to be able to adapt to new situations. This requires the algorithms to possess a certain degree of scalability and adaptability, enabling them to self-adjust and learn based on new data and environmental conditions.