بدائل البحث:
"defect detection algorithm" » "object detection algorithm" (توسيع البحث)
"neural coding algorithm" » "neural cosine algorithm" (توسيع البحث), "neural modeling algorithm" (توسيع البحث), "neural finding algorithm" (توسيع البحث)
"element data algorithms" » "element art algorithms" (توسيع البحث), "element all algorithms" (توسيع البحث), "element both algorithms" (توسيع البحث), "settlement data algorithms" (توسيع البحث), "relevant data algorithms" (توسيع البحث), "movement data algorithms" (توسيع البحث)
"defect detection algorithm" » "object detection algorithm" (توسيع البحث)
"neural coding algorithm" » "neural cosine algorithm" (توسيع البحث), "neural modeling algorithm" (توسيع البحث), "neural finding algorithm" (توسيع البحث)
"element data algorithms" » "element art algorithms" (توسيع البحث), "element all algorithms" (توسيع البحث), "element both algorithms" (توسيع البحث), "settlement data algorithms" (توسيع البحث), "relevant data algorithms" (توسيع البحث), "movement data algorithms" (توسيع البحث)
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21
SPDConv structure diagram.
منشور في 2025"…To address this problem, this paper proposes a YOLOv8-based insulator defect detection algorithm, YOLOv8-SSF. Firstly, SimAM (parameter-free attention mechanism) is included in the algorithm’s backbone network, which improves the ability to focus on critical features while maintaining a lightweight model. …"
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22
Dataset.
منشور في 2025"…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …"
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23
Loss curve comparison diagram.
منشور في 2025"…To address this problem, this paper proposes a YOLOv8-based insulator defect detection algorithm, YOLOv8-SSF. Firstly, SimAM (parameter-free attention mechanism) is included in the algorithm’s backbone network, which improves the ability to focus on critical features while maintaining a lightweight model. …"
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24
Visualisation and analysis of test results.
منشور في 2025"…To address this problem, this paper proposes a YOLOv8-based insulator defect detection algorithm, YOLOv8-SSF. Firstly, SimAM (parameter-free attention mechanism) is included in the algorithm’s backbone network, which improves the ability to focus on critical features while maintaining a lightweight model. …"
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25
Performance of different algorithms.
منشور في 2025"…This paper proposes a lightweight PCB defect detection algorithm based on YOLO. To address the problem of large numbers of parameters and calculations, GhostNet are used in Backbone to keep the model lightweight. …"
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26
SCUNet structured flowchart.
منشور في 2025"…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …"
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27
SCUNet Network structure diagram.
منشور في 2025"…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …"
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28
Comparison of mAP@0.5 and mAP@0.5:0.95 curves.
منشور في 2025"…To address this problem, this paper proposes a YOLOv8-based insulator defect detection algorithm, YOLOv8-SSF. Firstly, SimAM (parameter-free attention mechanism) is included in the algorithm’s backbone network, which improves the ability to focus on critical features while maintaining a lightweight model. …"
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29
Overall system framework.
منشور في 2025"…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …"
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30
Experimental parameter settings.
منشور في 2025"…This paper proposes a lightweight PCB defect detection algorithm based on YOLO. To address the problem of large numbers of parameters and calculations, GhostNet are used in Backbone to keep the model lightweight. …"
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31
Comparison table of filter algorithm parameters.
منشور في 2025"…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …"
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32
Detection results for the migrated application.
منشور في 2025"…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …"
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33
Revised yolov8 training process.
منشور في 2025"…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …"
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34
YOLOv8 improved network structure diagram.
منشور في 2025"…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …"
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35
Experimental environment configuration.
منشور في 2025"…This paper proposes a lightweight PCB defect detection algorithm based on YOLO. To address the problem of large numbers of parameters and calculations, GhostNet are used in Backbone to keep the model lightweight. …"
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36
Industrial smoke vapor.
منشور في 2025"…<div><p>In recent years, the demand for efficient and accurate defect detection algorithms in industrial production has been increasing. …"
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37
The network structure of our model.
منشور في 2025"…This paper proposes a lightweight PCB defect detection algorithm based on YOLO. To address the problem of large numbers of parameters and calculations, GhostNet are used in Backbone to keep the model lightweight. …"
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38
The structural layout of the Ghost module.
منشور في 2025"…This paper proposes a lightweight PCB defect detection algorithm based on YOLO. To address the problem of large numbers of parameters and calculations, GhostNet are used in Backbone to keep the model lightweight. …"
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39
The CBS structure improvement based on DSConv.
منشور في 2025"…This paper proposes a lightweight PCB defect detection algorithm based on YOLO. To address the problem of large numbers of parameters and calculations, GhostNet are used in Backbone to keep the model lightweight. …"
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40
The improved C3STR structure.
منشور في 2025"…This paper proposes a lightweight PCB defect detection algorithm based on YOLO. To address the problem of large numbers of parameters and calculations, GhostNet are used in Backbone to keep the model lightweight. …"