Quality inspection and defect detection remain critical challenges across diverse industrial
applications. Driven by advancements in Deep Learning, Convolutional Neural Networks …

The identification of archaeological ceramics is a relevant topic in the field of cultural
heritage, and the history of archaeological ceramics can be traced back to prehistoric times …

Artificial intelligence models are currently being proposed for application in improving
performance in addressing contemporary management and production issues. With the goal …

Defect detection is a key element of quality control in today's industries, and the process
requires the incorporation of automated methods, including image sensors, to detect any …

Defect detection on rail lines is essential for ensuring safe and efficient transportation.
Current image analysis methods with deep neural networks (DNNs) for defect detection …

As manifestations of Industry 4.0. become visible across various applications, one key and
opportune area of development are quality inspection processes and defect detection. Over …

Welding is an extensively used technique in manufacturing, and as for every other process,
there is the potential for defects in the weld joint that could be catastrophic to the …

Strip steel plays a crucial role in modern industrial production, where enhancing the
accuracy and real-time capabilities of surface defect classification is essential. However …

Nowadays, the rapid development of AI models (Artificial Intelligence) has significantly
improved performance in addressing construction management challenges in daily life …

Existing deep learning methods for rail surface defect detection face issues such as poor
compatibility with embedded detection systems, high computational resource consumption …