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 model's ability to identify fine-grained symbol details and a Proximity-aware Loss Function to improve performance in cluttered regions by motivation of this work stems penalizing misclassifications based on the spatial proximity of neighboring symbols. These modifications result in an optimized method for semantic processing in symbol classification tasks. The proposed model achieves 93.5% mean average precision (mAP) a 3.8% improvement over the top-performing baseline, alongside a 19.6% reduction in model parameters. These advancements contribute to more efficient and accurate semantic processing of SLDs, paving the way for improved analysis of electrical system diagrams.
Bhanbhro et al. (Wed,) studied this question.