ESC-YOLOv8: An enhanced deep learning framework for semantic understanding of single-line diagram imagery
Hina Bhanbhro, Yew Kwang Hooi, Worapan Kusakunniran, M Nordin B. Zakaria, Syed Abdul Moiz Hashmi, Zaira Hassan Amur, Vengas Memon
TL;DR
This paper introduces ESC-YOLOv8, a deep learning framework that improves the accuracy and efficiency of interpreting single-line electrical diagrams.
Contribution
The novel Hybrid Residual Attention Module and Proximity-aware Loss Function enhance symbol classification in single-line diagrams.
Findings
The proposed model achieves 93.5% mean average precision, a 3.8% improvement over existing methods.
The model reduces parameters by 19.6%, making it more efficient for semantic processing tasks.
Abstract
Accurate interpretation of single-line diagrams (SLDs) is crucial for analyzing electrical systems, as they encapsulate vital information about operational safety and efficiency in a simplified format. Traditional SLD processing methods rely on manual inspection and basic image analysis, which are computationally intensive, error-prone, and require extensive preprocessing. Although deep learning has been applied to symbol classification, existing models often fail to capture fine-grained symbol details, leading to misclassification. To address these limitations, this study proposes a hybrid deep learning-based symbol classification method. A newly created dataset was benchmarked using state-of-the-art deep learning models, and an optimal model was systematically designed, developed, and tested. The proposed approach integrates a Hybrid Residual Attention Module (HRAM) to enhance the…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Advanced Neural Network Applications · Image and Object Detection Techniques