This paper proposes a lightweight detection model based on machine vision, YOLOv5-GC, to improve the efficiency and accuracy of detecting classifying surface defects in preforming materials. During this process, clear images entire are difficult obtain due stickiness, high reflectivity, black resin thermosetting plain woven prepreg. To address challenge, we built vision platform equipped with l...

For a long time, the detection and extraction of printed surface defects has been hot issue in print industry. Nowadays, defect large number products still relies on traditional image processing algorithms such as scale invariant feature transform (SIFT) oriented fast rotated brief (ORB), researchers need to design for specific products. At present, based object have applied but lots labeling s...

In this paper, a modified YOLOv3 net has been proposed for surface defect detection. Different from other pixel-level segmenting methods, locates the regions of defects with bounding rectangles. Compared conventional detectors, operating efficiency is rather high without generating region proposals by sliding boxes. Although details are omitted in process, primary information location detects a...

Traditional vision methods for solar cell defect detection have problems such as low accuracy and few types of detection, so this paper proposes an optimized YOLOv5 model more accurate comprehensive identification defects in cells. The firstly integrates five data enhancement methods, namely Mosaic, Mixup, hsv transform, scale transform flip, to expand the existing set improve feature training ...