Research on YOLOv5 Oracle Recognition Algorithm Based on Multi-Module Fusion

The recognition of oracle bone script is of significant importance for understanding the evolution of Chinese characters, their morphological features, and semantic changes. However, traditional methods and some deep learning models have limited ability to capture the complex forms and fine details...

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Bibliographic Details
Published in:IEEE access Vol. 13; pp. 24358 - 24367
Main Authors: Zhang, Xinhang, Ma, Zhenhua, Zhang, Yaru, Ru, Huiying
Format: Journal Article
Language:English
Published: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Attention mechanisms
BiFPN_SDI
Bones
C3_DAttention
Character recognition
Convolution
detect_efficient
Evolutionary algorithms
Feature extraction
Machine learning
Neck
Object recognition
Optimization
oracle recognition
Target detection
Text recognition
Transforms
YOLO
YOLOv5
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:The recognition of oracle bone script is of significant importance for understanding the evolution of Chinese characters, their morphological features, and semantic changes. However, traditional methods and some deep learning models have limited ability to capture the complex forms and fine details of oracle bone script, which makes it difficult to fully detect subtle differences between characters. Additionally, models trained on such data tend to struggle with recognizing rare or unseen characters, often leading to recognition errors. Therefore, improving the robustness of these models is essential. This paper presents a novel recognition algorithm based on YOLOv5, incorporating BiFPN-SDI, C3-DAttention, and Detect_Efficient to significantly enhance detection performance. BiFPN-SDI enables more precise feature fusion and attention mechanisms, improving the detection of small targets. C3-DAttention combines channel and spatial attention mechanisms to enhance feature extraction in deep convolutional neural networks. Detect_Efficient further improves the model's detection and recognition capabilities. Experimental results show that the proposed improvements lead to a 0.7% increase in precision, a 1.1% increase in recall, and a 0.3% improvement in MAP@50. Furthermore, the model's parameter count is reduced to 1,009,668, and its processing speed is increased to 90 fps, significantly improving the ability to extract and recognize features in oracle bone script.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3536553