Wind Turbine Surface Defect Detection Method Based on YOLOv5s-L
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset Preparation
2.2. The Experimental Environment
2.3. Experimental Parameter Setting
2.4. YOLOv5 Algorithm Improvement
2.4.1. C2f Module Improvement
2.4.2. Neck Network Improvement
2.4.3. Classification Loss Function Improvement
2.5. Evaluating Indicator
3. Results
3.1. Comparison of Detection Algorithms
3.2. Contrast Analysis of Detection Effect
4. Conclusions
- (1)
- The introduction of C2f modules to optimize the neural network, increasing the accuracy;
- (2)
- The SE attention mechanism extracts important characteristic information and enhances attention to small targets;
- (3)
- BiFPN is introduced to optimize Neck networks for multi-scale fusion;
- (4)
- DWconv ensures lightweight network accuracy.
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Hardware/System | Model/Version |
---|---|
Operating system | Windows 10 |
CPU | Intel Core i7-8700 |
GPU | NVIDIA GeForce RTX 2080Ti 32 GB |
Deep Learning Framework | Pytorch 1.13.1 |
Evelopment language | Python 3.8 |
Algorithms | C2f | SE | BiFPN | DWconv | Focal-Loss | mAP@0.5 | Weight (m) |
---|---|---|---|---|---|---|---|
YOLOv5s- | - | - | - | - | - | 0.839 | 13.7 |
YOLOv5s-C2f | * | - | - | - | - | 0.846 | 14.9 |
YOLOv5s-SE | - | * | - | - | - | 0.843 | 13.8 |
YOLOv5s-BiFPN | - | - | * | - | - | 0.842 | 13.7 |
YOLOv5s-DW | - | - | - | * | - | 0.838 | 12.2 |
YOLOv5s-F | - | - | - | - | * | 0.841 | 13.7 |
YOLOv5s-L | * | * | * | * | * | 0.858 | 13.9 |
Algorithms | mAP@0.5 | Weight (m) |
---|---|---|
Faster R-CNN | 0.877 | 331.1 |
SSD | 0.782 | 181 |
YOLOV5s | 0.839 | 13.7 |
YOLOV5s-L | 0.858 | 13.9 |
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Liu, C.; An, C.; Yang, Y. Wind Turbine Surface Defect Detection Method Based on YOLOv5s-L. NDT 2023, 1, 46-57. https://doi.org/10.3390/ndt1010005
Liu C, An C, Yang Y. Wind Turbine Surface Defect Detection Method Based on YOLOv5s-L. NDT. 2023; 1(1):46-57. https://doi.org/10.3390/ndt1010005
Chicago/Turabian StyleLiu, Chang, Chen An, and Yifan Yang. 2023. "Wind Turbine Surface Defect Detection Method Based on YOLOv5s-L" NDT 1, no. 1: 46-57. https://doi.org/10.3390/ndt1010005