In the face of detection problems posed by complex textile texture backgrounds, different sizes, and types defects, commonly used object networks have limitations in handling target sizes. Furthermore, their stability anti-jamming capabilities are relatively weak. Therefore, when more diverse, false detections or missed likely to occur. order meet stringent requirements defect detection, we pro...

Background: Deep learning is a very fast-growing field in the area of artificial intelligence with remarkable results recent years. Many works lithography and photomask have shown progress technology this application potential to improve by automation processes. Despite progress, use machine techniques mask repair still seems be at beginning. Aim: We show that deep learning-based methods can su...

Steel surface defect detection is crucial for ensuring steel quality. The traditional algorithm has low probability. This paper proposes an improved based on the YOLOv5 model to enhance Firstly, deformable convolution introduced in backbone network, and a module replaced by convolution; secondly, CBAM attention mechanism added network; then, Focal EIOU used instead of CIOU loss function YOLOv5;...

The lifetime of solid-state image sensors is limited by the appearance of defects, particularly hot-pixels, which we have previously shown to develop continuously over the sensor lifetime. Analysis based on spatial distribution and temporal growth of defects displayed no evidence of the defects being caused by material degradation. Instead, high radiation appears to accelerate defect developmen...

This work explores potential methods for use in the detection and classification of defects within crystal structures via analysis of diffuse scattering data generated by single crystal neutron scattering experiments. The proposed defect detection methodology uses machine learning and image processing techniques to perform image texture analysis on neutron diffraction patterns generated by neut...

In this paper, a novel defect detection scheme based on morphological filters is proposed to tackle the problem of automated defect detection for woven fabrics. In the proposed scheme, important texture features of the textile fabric are extracted using a pre-trained Gabor wavelet network. These texture features are then used to facilitate the construction of structuring elements in subsequent ...

Sanitary ceramic products, such as toilet and wash basin, are widely used in our daily life. ceramics expected to have some excellent physical properties, corrosion resistance, easy cleaning, low water absorption. However, surface defects sanitary inevitable due complex production processes changing environment. Therefore, defect detection must be performed the manufacturing process of ceramics...

Some types of laser range scanner can measure range and color data simultaneously, and are often used to acquire 3D structure of outdoor scenery. However, unfortunately a laser range scanner cannot give us perfect range information about the target objects such as buildings, and various factors incur critical defects of range data. We present a defect detection scheme based on region segmentati...

The three-dimensional defect distribution in material test specimens is a very important piece of information for us to understand the deformation and failure mechanism of materials. This distribution is sometimes complicated by the surface roughness of specimens in the defect detection of computed tomography data. In this paper, we proposed a new local differentiation algorithm to remove the s...

The automated defect detection of jacquard fabrics is intuitively appealing to the textile industry but as yet there has been little in-depth research in this field. In this paper, we introduce a defect detection method for use on jacquard fabrics that is based on multiple color-channel analysis. According to the number of color yarns employed, a number of color channels can be extracted from a...