Over the last few decades, detecting surface defects has attracted significant attention as a
challenging task. There are specific classes of problems that can be solved using traditional …

The comprehensive intelligent development of the manufacturing industry puts forward new
requirements for the quality inspection of industrial products. This paper summarizes the …

Effective monitoring of manufacturing processes is crucial for maintaining product quality
and operational efficiency. Modern manufacturing environments often generate vast …

Plant disease detection is essential for optimizing agricultural productivity and crop quality.
With the recent advent of deep learning and large-scale plant disease datasets, many …

Anomaly detection has gained significant attention with the advancements in deep neural
networks. Effective training requires both normal and anomalous data, but this often leads to …

Deep learning methods are currently used in industries to improve the efficiency and quality
of the product. Detecting defects on printed circuit boards (PCBs) is a challenging task and is …

A surface micro-defect is characterized by a small size and a susceptibility to noise. Micro-
defect detection and classification is very challenging. This paper proposes a Micro-defect …

Anomaly detection systems based on artificial intelligence (AI) have demonstrated high
performance and efficiency in a wide range of applications such as power plants and smart …

Scanning tunneling microscopy (STM) imaging has been routinely applied in studying
surface nanostructures owing to its capability of acquiring high-resolution molecule-level …

In this paper, we presented an approach to deploying a student–teacher feature pyramid
model (STFPM) for anomaly detection metal-textile gas filters used in automotive exhaust …