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ÇѱÛÁ¦¸ñ(Korean Title) |
Application of YOLOv5 Neural Network Based on Improved Attention Mechanism in Recognition of Thangka Image Defects |
¿µ¹®Á¦¸ñ(English Title) |
Application of YOLOv5 Neural Network Based on Improved Attention Mechanism in Recognition of Thangka Image Defects |
ÀúÀÚ(Author) |
Hanwen Guo
Ziyang Liu
Zeyu Jiao
Yao Fan
Yubo Li
Yingnan Shi
Shuaishuai Wang
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¿ø¹®¼ö·Ïó(Citation) |
VOL 16 NO. 01 PP. 0245 ~ 0265 (2022. 01) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
In response to problems such as insufficient extraction information, low detection accuracy, and frequent misdetection in the field of Thangka image defects, this paper proposes a YOLOv5 prediction algorithm fused with the attention mechanism. Firstly, the Backbone network is used for feature extraction, and the attention mechanism is fused to represent different features, so that the network can fully extract the texture and semantic features of the defect area. The extracted features are then weighted and fused, so as to reduce the loss of information. Next, the weighted fused features are transferred to the Neck network, the semantic features and texture features of different layers are fused by FPN, and the defect target is located more accurately by PAN. In the detection network, the CIOU loss function is used to replace the GIOU loss function to locate the image defect area quickly and accurately, generate the bounding box, and predict the defect category. The results show that compared with the original network, YOLOv5-SE and YOLOv5-CBAMachieve an improvement of 8.95% and 12.87% in detection accuracy respectively. The improved networks can identify the location and category of defects more accurately, and greatly improve the accuracy of defect detection of Thangka images. |
Å°¿öµå(Keyword) |
Online Travel Agency
Text Analysis
Word Cloud
The Five Great Mountains
Tourist Satisfaction
YOLOv5
Defect Detection
Thangka Image
Deep Learning
SE
CBAM
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