To address the problem of low efficiency for manual detection in the defect detection field for
metal shafts, we propose a deep learning defect detection method based on the improved …

Early and effective surface defect detection in industrial components can avoid the
occurrence of serious safety hazards. Since most industrial component surfaces have tiny …

Purpose The current popular image processing technologies based on convolutional neural
network have the characteristics of large computation, high storage cost and low accuracy …

The detection of surface defects in metal materials has been a challenging issue in the
industrial domain. The existing algorithms for metal surface defect detection are limited to a …

Surface defect detection in precision metal workpieces is critical for ensuring product quality.
Due to the weak and diverse defect object area in precision metal workpieces, they lead to …

Due to the presence of numerous surface defects, the inadequate contrast between
defective and non-defective regions, and the resemblance between noise and subtle …

In the digital manufacturing industry, since various features of steel products cannot quantify
through traditional algorithms of quality evaluation, deep learning has been introduced into …

Aiming at the problems of low efficiency and poor accuracy in the product surface defect
detection. In this paper, an online surface defects detection method based on YOLOV3 is …

In the production and manufacturing industry, factors such as rolling equipment and
processes may cause various defects on the surface of the steel plate, which greatly affect …

Surface defects, which often occur during the production of aluminum profiles, can directly
affect the quality of aluminum profiles, and should be monitored in real time. This paper …