Improved YOLOV5 Angle Embossed Character Recognition by Multiscale Residual Attention with Selectable Clustering

In the intelligentization process of power transmission towers, automated identification of stamped characters is crucial. Currently, manual methods are predominantly used, which are time-consuming, labor-intensive, and prone to errors. For small-sized characters that are incomplete, connected, and...

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Bibliographic Details
Published inElectronics (Basel) Vol. 13; no. 13; p. 2435
Main Authors Ying, Shenshun, Fang, Jianhai, Tang, Shaozhang, Bao, Wenzhi
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2024
Subjects
Accuracy
Algorithms
Artificial neural networks
Automation
Clustering
Deep learning
Design
Electricity distribution
Embossing
Error reduction
Handwriting
Human error
Manufacturing
Methods
Neural networks
Object recognition
Optical character recognition
Quality control
Random errors
Shape recognition
Target detection
Target recognition
Transmission towers
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Summary:In the intelligentization process of power transmission towers, automated identification of stamped characters is crucial. Currently, manual methods are predominantly used, which are time-consuming, labor-intensive, and prone to errors. For small-sized characters that are incomplete, connected, and irregular in shape, existing OCR technologies also struggle to achieve satisfactory recognition results. Thus, an approach utilizing an improved deep neural network model to enhance the recognition performance of stamped characters is proposed. Based on the backbone network of YOLOv5, a multi-scale residual attention encoding mechanism is introduced during the upsampling process to enhance the weights of small and incomplete character targets. Additionally, a selectable clustering minimum iteration center module is introduced to optimize the selection of clustering centers and integrate multi-scale information, thereby reducing random errors. Experimental verification shows that the improved model significantly reduces the instability caused by random selection of clustering centers during the clustering process, accelerates the convergence of small target recognition, achieves a recognition accuracy of 97.6% and a detection speed of 43 milliseconds on the task of stamped character recognition, and significantly outperforms existing Fast-CNN, YOLOv5, and YOLOv6 models in terms of performance, effectively enhancing the precision and efficiency of automatic identification.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13132435