Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG19

Understand

Modern industry requires modern solutions for monitoring the automatic production of goods.

• Smart monitoring of the functionality of the mechanical parts of technology systems or machines is mandatory for a fully automatic production process.
• Although Deep Learning has been advancing, allowing for real-time object detection and other tasks, little has been investigated about the effectiveness of specially designed Convolutional Neural Networks for defect detection and industrial object recognition.
• In the particular study, we employed six publically available industrial-related datasets containing defect materials and industrial tools or engine parts, aiming to develop a specialized model for pattern recognition.