Object Detection Algorithm for Surface Defects Based on a Novel YOLOv3 Model

The surface defects of industrial structural parts have the characteristics of a large-scale span and many small objects, so a novel YOLOv3 model, the YOLOv3-ALL algorithm, is proposed in this paper to solve the problem of precise defect detection. The K-means++ algorithm is combined with the inters...

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Vydáno v:Processes Ročník 10; číslo 4; s. 701
Hlavní autoři: Lv, Ning, Xiao, Jian, Qiao, Yujing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.04.2022
Témata:
Algorithms
Artificial intelligence
Boxes
Clustering
Datasets
Deep learning
Defects
Intersections
Object recognition
Semantics
Surface defects
Vision systems
ISSN:2227-9717, 2227-9717
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Shrnutí:The surface defects of industrial structural parts have the characteristics of a large-scale span and many small objects, so a novel YOLOv3 model, the YOLOv3-ALL algorithm, is proposed in this paper to solve the problem of precise defect detection. The K-means++ algorithm is combined with the intersection-over-union (IoU) and comparison of the prior box for clustering, which improves the clustering effect. The convolutional block attention module (CBAM) is embedded in the network, thus improving the ability of the network to obtain key information in the image. By adding fourth-scale prediction, the detection capability of a YOLOv3 network for small-object defects is greatly improved. A loss function is designed, which adds the generalized intersection-over-union (GIoU) loss combined with focal loss to solve the problems of L2 loss and class imbalance in samples. Experiments regarding contour-defect detection for stamping parts show that the mean average precision (mAP) of the YOLOV3-ALL algorithm reaches 75.05% in defect detection, which is 25.16% higher than that of the YOLOv3 algorithm. The average detection time is 39 ms/sheet. This proves that the YOLOv3-ALL algorithm has good real-time detection efficiency and high detection accuracy.
Bibliografie:ObjectType-Article-1
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr10040701